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docs/src/home.md

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[DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/)
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[ODE/SDE/jump solver](@ref ref), and can be used within `EnsembleProblem`s for carrying
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out [parallelized parameter sweeps and statistical sampling](@ref ref). Plot recipes
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are available for [visualizing of all solutions](@ref ref).
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are available for [visualization of all solutions](@ref ref).
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- Non-integer (e.g. `Float64`) stoichiometric coefficients [are supported](@ref ref) for generating
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ODE models, and symbolic expressions for stoichiometric coefficients [are supported](@ref ref) for
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all system types.
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- A [network analysis suite](@ref ref) permits the computation of linkage classes, deficiencies, and
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reversibilities.
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- [Conservation laws can be detected and utilised](@ref ref) to reduce system sizes, and to generate
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- [Conservation laws can be detected and utilized](@ref ref) to reduce system sizes, and to generate
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non-singular Jacobians (e.g. during conversion to ODEs, SDEs, and steady state equations).
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- Catalyst reaction network models can be [coupled with differential and algebraic equations](@ref ref)
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(which are then incorporated during conversion to ODEs, SDEs, and steady state equations).
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SDE simulations](@ref ref).
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- [JumpProcesses.jl](https://github.com/SciML/JumpProcesses.jl) Can be used to [model jump
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simulations](@ref ref).
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- Support for [parallelisation of all simulations]((@ref ref)), including parallelisation of
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- Support for [parallelization of all simulations](@ref ref), including parallelization of
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[ODE simulations on GPUs](@ref ref) using
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[DiffEqGPU.jl](https://github.com/SciML/DiffEqGPU.jl).
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- [Latexify](https://korsbo.github.io/Latexify.jl/stable/) can be used to [generate LaTeX
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expressions](@ref ref) corresponding to generated mathematical models or the
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underlying set of reactions.
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- [Graphviz](https://graphviz.org/) can be used to generate and [visualize reaction network graphs](@ref ref)
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(reusing the Graphviz interface created in [Catlab.jl](https://algebraicjulia.github.io/Catlab.jl/stable/).)
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- Models steady states can be computed through homotopy continuation using [HomotopyContinuation.jl](https://github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl)
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- Model steady states can be computed through homotopy continuation using [HomotopyContinuation.jl](https://github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl)
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(which can find *all* steady states of systems with multiple ones), by forward ODE simulations using
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[SteadyStateDiffEq.jl)](https://github.com/SciML/SteadyStateDiffEq.jl), or by nonlinear systems
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solving using [NonlinearSolve.jl](https://github.com/SciML/NonlinearSolve.jl).
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- [Optimization.jl](https://github.com/SciML/Optimization.jl), [DiffEqParamEstim.jl](https://github.com/SciML/DiffEqParamEstim.jl),
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and [PEtab.jl](https://github.com/sebapersson/PEtab.jl) can all be used to [fit model parameters to data](@ref ref).
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- [GlobalSensitivity.jl](https://github.com/SciML/GlobalSensitivity.jl) can be used to perform
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[global sensitivity analysis](@ref ref) of model behaviours.
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[global sensitivity analysis](@ref ref) of model behaviors.
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#### Features of packages built upon Catalyst
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- Catalyst [`ReactionSystem`](@ref)s can be [imported from SBML files](@ref ref) via
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- [DelaySSAToolkit.jl](https://github.com/palmtree2013/DelaySSAToolkit.jl) can
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augment Catalyst reaction network models with delays, and can simulate the
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resulting stochastic chemical kinetics with delays models.
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- [BondGraphs.jl](https://github.com/jedforrest/BondGraphs.jl) a package for
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- [BondGraphs.jl](https://github.com/jedforrest/BondGraphs.jl), a package for
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constructing and analyzing bond graphs models, which can take Catalyst models as input.
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- [PEtab.jl](https://github.com/sebapersson/PEtab.jl) a package that implements the PEtab format for
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- [PEtab.jl](https://github.com/sebapersson/PEtab.jl), a package that implements the PEtab format for
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fitting reaction network ODEs to data. Input can be provided either as SBML files or as Catalyst
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`ReactionSystem`s.
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## [How to read this documentation](@id doc_home_documentation)
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The Catalyst documentation is separated into sections describing Catalyst's various features. Where appropriate, some sections will also give advice on best practices for various modelling workflows, and provide links with further reading. Each section also contains a set of relevant example workflows. Finally, the [API](@ref api) section contains a list of all functions exported by Catalyst (as well as descriptions of them and their inputs and outputs).
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The Catalyst documentation is separated into sections describing Catalyst's various features. Where appropriate, some sections will also give advice on best practices for various modeling workflows, and provide links with further reading. Each section also contains a set of relevant example workflows. Finally, the [API](@ref api) section contains a list of all functions exported by Catalyst (as well as descriptions of them and their inputs and outputs).
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New users are recommended to start with either the [Introduction to Catalyst and Julia for New Julia users](@ref catalyst_for_new_julia_users) or [Introduction to Catalyst](@ref introduction_to_catalyst) sections (depending on whether they are familiar with Julia programming or not). This should be enough to carry out many basic Catalyst workflows. Next, [The Catalyst DSL](@ref ref) section gives a more throughout introduction to model creation, while the [Introduction to model simulation](@ref ref) section more through describes how simulations are carried out. Once you have gotten started using Catalyst, you can read whichever sections are relevant to your work (they should all be clearly labelled).
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1 + 2
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nothing # hide
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```
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and
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and here:
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```@example home1
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@reaction_network begin
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(p,d), 0 <--> X
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```
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is also needed.
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A more throughout guide for setting up Catalyst and installing Julia packages can be found [here](@ref ref).
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A more throughout guide for setting up Catalyst and installing Julia packages can be found [here](@ref catalyst_for_new_julia_users_packages).
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## Illustrative example
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```
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## Elaborate example
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In the above example, we used basic Catalyst-based workflows to simulate a simple model. Here we instead show how various Catalyst features can compose to create a much more advanced model. Our model describes how the volume of a cell ($V$) is affected by a growth factor ($G$). The growth factor only promotes growth while in its phosphorylated form ($Gᴾ$). The phosphorylation of $G$ ($G \to Gᴾ$) is promoted by sunlight (modelled as the cyclic sinusoid $kₐ*(sin(t)+1)$) phosphorylates the growth factor (producing $Gᴾ$). When the cell reaches a critical volume ($V$) it goes through cell division. First, we declare our model:
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In the above example, we used basic Catalyst-based workflows to simulate a simple model. Here we
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instead show how various Catalyst features can compose to create a much more advanced model. Our
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model describes how the volume of a cell ($V$) is affected by a growth factor ($G$). The growth
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factor only promotes growth while in its phosphorylated form ($Gᴾ$). The phosphorylation of $G$
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($G \to Gᴾ$) is promoted by sunlight (modeled as the cyclic sinusoid $kₐ*(sin(t)+1)$), which
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phosphorylates the growth factor (producing $Gᴾ$). When the cell reaches a critical volume ($V$)
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it undergoes through cell division. First, we declare our model:
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```@example home_elaborate_example
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using Catalyst
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cell_model = @reaction_network begin
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sol = solve(sprob, STrapezoid(); seed = 1234) # hide
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plot(sol; xguide = "Time (au)", lw = 2)
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```
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![Elaborate SDE simulation](docs/src/assets/readme_elaborate_sde_plot.svg)
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## [Getting Help](@id doc_home_help)
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Catalyst developers are active on the [Julia Discourse](https://discourse.julialang.org/),

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