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Merge pull request #768 from SciML/doc_updates
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HISTORY.md

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# Breaking updates and feature summaries across releases
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## Catalyst unreleased (master branch)
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## Catalyst 14.0
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- Simulation of spatial ODEs now supported. For full details, please see https://github.com/SciML/Catalyst.jl/pull/644 and upcoming documentation. Note that these methods are currently considered alpha, with the interface and approach changing even in non-breaking Catalyst releases.
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- LatticeReactionSystem structure represents a spatial reaction network:
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```julia
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k5, 0 --> X
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end
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using BifurcationKit
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bif_par = :k1
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u_guess = [:X => 5.0, :Y => 2.0]

Project.toml

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RuntimeGeneratedFunctions = "0.5.12"
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Setfield = "1"
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SymbolicUtils = "1.0.3"
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Symbolics = "5.0.3"
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Symbolics = "5.14"
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Unitful = "1.12.4"
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julia = "1.9"
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README.md

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## Breaking changes and new features
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**NOTE:** version 13 is a breaking release, with changes to simplify the DSL
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notation while also adding more features, changes to how chemical species are
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specified symbolically when directly building `ReactionSystem`s, and changes that
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simplify how to include ODE or algebraic constraint equation.
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**NOTE:** version 14 is a breaking release, prompted by the release of ModelingToolkit.jl version 9. This caused several breaking changes in how Catalyst models are represented and interfaced with.
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Breaking changes and new functionality are summarized in the
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[HISTORY.md](HISTORY.md) file.
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Several Youtube video tutorials and overviews are also available, but note these use older
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Catalyst versions with slightly different notation (for example, in building reaction networks):
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- From JuliaCon 2023: A short 15 minute overview of Catalyst as of version 13 is
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available in the talk [Catalyst.jl, Modeling Chemical Reaction Networks](https://www.youtube.com/watch?v=yreW94n98eM&ab_channel=TheJuliaProgrammingLanguage).
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- From JuliaCon 2022: A three hour tutorial workshop overviewing how to use
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Catalyst and its more advanced features as of version 12.1. [Workshop
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video](https://youtu.be/tVfxT09AtWQ), [Workshop Pluto.jl
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Modelling of Biochemical Reaction
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Networks](https://www.youtube.com/watch?v=s1e72k5XD6s)
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Finally, an overview of the package and its features (as of version 13) can also be found in its corresponding research paper, [Catalyst: Fast and flexible modeling of reaction networks](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530).
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## Features
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- A DSL provides a simple and readable format for manually specifying chemical

docs/Project.toml

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HomotopyContinuation = "2.6"
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Latexify = "0.15, 0.16"
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ModelingToolkit = "8.47"
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NonlinearSolve = "1.6.0, 2"
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NonlinearSolve = "3.4.0"
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Optim = "1"
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Optimization = "3.19"
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OptimizationOptimisers = "0.1.1"
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OrdinaryDiffEq = "6"
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PEtab = "2"
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Plots = "1.36"
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SciMLBase = "~2.5"
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SciMLBase = "2.13"
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SciMLSensitivity = "7.19"
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Setfield = "1.1"
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SpecialFunctions = "2.1"
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SteadyStateDiffEq = "1"
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SteadyStateDiffEq = "2.0.1"
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StochasticDiffEq = "6"
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Symbolics = "5.0.3"
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Symbolics = "5.14"

docs/src/catalyst_applications/bifurcation_diagrams.md

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# [Bifurcation Diagrams](@id bifurcation_diagrams)
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Bifurcation diagrams describe how, for a dynamical system, the quantity and type of its steady states change as a parameter is varied[^1]. When using Catalyst-generated models, these can be computed with the [BifurcationKit.jl](https://github.com/bifurcationkit/BifurcationKit.jl) package. Catalyst provides a simple interface for creating BifurcationKit compatible `BifurcationProblem`s from `ReactionSystem`s. If you use this feature in your research, please [cite the BifurcationKit.jl](@ref bifurcation_kit_citation) and [Catalyst.jl](@ref catalyst_citation) publications.
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Bifurcation diagrams describe how, for a dynamical system, the quantity and type of its steady states change as a parameter is varied[^1]. When using Catalyst-generated models, these can be computed with the [BifurcationKit.jl](https://github.com/bifurcationkit/BifurcationKit.jl) package. Catalyst provides a simple interface for creating BifurcationKit compatible `BifurcationProblem`s from `ReactionSystem`s.
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This tutorial briefly introduces how to use Catalyst with BifurcationKit through basic examples, with BifurcationKit.jl providing [a more extensive documentation](https://bifurcationkit.github.io/BifurcationKitDocs.jl/stable/). Especially for more complicated systems, where careful tuning of algorithm options might be required, reading the BifurcationKit documentation is recommended. Finally, BifurcationKit provides many additional features not described here, including [computation of periodic orbits](https://bifurcationkit.github.io/BifurcationKitDocs.jl/stable/periodicOrbit/), [tracking of bifurcation points along secondary parameters](https://bifurcationkit.github.io/BifurcationKitDocs.jl/dev/branchswitching/), and [bifurcation computations for PDEs](https://bifurcationkit.github.io/BifurcationKitDocs.jl/dev/tutorials/tutorials/#PDEs:-bifurcations-of-equilibria).
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docs/src/catalyst_applications/homotopy_continuation.md

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*homotopy continuation* algorithm. This is implemented in Julia through the
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[HomotopyContinuation.jl](https://www.juliahomotopycontinuation.org/) package.
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Catalyst contains a special homotopy continuation extension that is loaded whenever HomotopyContinuation.jl is. This exports a single function, `hc_steady_states`, that can be used to find the steady states of any `ReactionSystem` structure. If you use this in your research, please [cite the HomotopyContinuation.jl](@ref homotopy_continuation_citation) and [Catalyst.jl](@ref catalyst_citation) publications.
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Catalyst contains a special homotopy continuation extension that is loaded whenever HomotopyContinuation.jl is. This exports a single function, `hc_steady_states`, that can be used to find the steady states of any `ReactionSystem` structure.
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## Basic example
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For this tutorial, we will use a model from Wilhelm (2009)[^1] (which

docs/src/catalyst_applications/nonlinear_solve.md

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However, if all (or multiple) steady states are sought, using homotopy continuation is better.
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This tutorial describes how to create `NonlinearProblem`s from Catalyst's `ReactionSystemn`s, and how to solve them using NonlinearSolve. More extensive descriptions of available solvers and options can be found in [NonlinearSolve's documentation](https://docs.sciml.ai/NonlinearSolve/stable/). If you use this in your research, please [cite the NonlinearSolve.jl](@ref nonlinear_solve_citation) and [Catalyst.jl](@ref catalyst_citation) publications.
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This tutorial describes how to create `NonlinearProblem`s from Catalyst's `ReactionSystemn`s, and how to solve them using NonlinearSolve. More extensive descriptions of available solvers and options can be found in [NonlinearSolve's documentation](https://docs.sciml.ai/NonlinearSolve/stable/).
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## Basic example
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Let us consider a simple dimerisation network, where a protein ($P$) can exist in a monomer and a dimer form. The protein is produced at a constant rate from its mRNA, which is also produced at a constant rate.

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