diff --git a/README.md b/README.md
index 24f172a91c..4017eeda1d 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
# Catalyst.jl
-[](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
[](https://docs.sciml.ai/Catalyst/stable/)
[](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/)
-
+[](https://docs.sciml.ai/Catalyst/stable/)
+[](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/)
+[](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
[](https://github.com/SciML/Catalyst.jl/actions?query=workflow%3ACI)
[](https://codecov.io/gh/SciML/Catalyst.jl)
@@ -12,183 +12,239 @@
[](https://github.com/SciML/ColPrac)
[](https://github.com/SciML/SciMLStyle)
+[](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530)
-Catalyst.jl is a symbolic modeling package for analysis and high performance
+Catalyst.jl is a symbolic modeling package for analysis and high-performance
simulation of chemical reaction networks. Catalyst defines symbolic
[`ReactionSystem`](https://docs.sciml.ai/Catalyst/stable/catalyst_functionality/programmatic_CRN_construction/)s,
which can be created programmatically or easily
-specified using Catalyst's domain specific language (DSL). Leveraging
-[ModelingToolkit](https://github.com/SciML/ModelingToolkit.jl) and
+specified using Catalyst's domain-specific language (DSL). Leveraging
+[ModelingToolkit.jl](https://github.com/SciML/ModelingToolkit.jl) and
[Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl), Catalyst enables
large-scale simulations through auto-vectorization and parallelism. Symbolic
`ReactionSystem`s can be used to generate ModelingToolkit-based models, allowing
the easy simulation and parameter estimation of mass action ODE models, Chemical
Langevin SDE models, stochastic chemical kinetics jump process models, and more.
Generated models can be used with solvers throughout the broader
-[SciML](https://sciml.ai) ecosystem, including higher level SciML packages (e.g.
+[SciML](https://sciml.ai) ecosystem, including higher-level SciML packages (e.g.
for sensitivity analysis, parameter estimation, machine learning applications,
etc).
## Breaking changes and new features
-**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.
+**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.
-Breaking changes and new functionality are summarized in the
-[HISTORY.md](HISTORY.md) file. Furthermore, a migration guide on how to adapt your workflows to the
-the new v14 update can be found [here](https://docs.sciml.ai/Catalyst/stable/v14_migration_guide/).
+Breaking changes and new functionality are summarized in the [HISTORY.md](HISTORY.md) file. Furthermore, a migration guide on how to adapt your workflows to the new v14 update can be found [here](https://docs.sciml.ai/Catalyst/stable/v14_migration_guide/).
## Tutorials and documentation
-The latest tutorials and information on using the package are available in the [stable
+The latest tutorials and information on using Catalyst are available in the [stable
documentation](https://docs.sciml.ai/Catalyst/stable/). The [in-development
documentation](https://docs.sciml.ai/Catalyst/dev/) describes unreleased features in
the current master branch.
-Several Youtube video tutorials and overviews are also available, but note these use older
-Catalyst versions with slightly different notation (for example, in building reaction networks):
-- From JuliaCon 2023: A short 15 minute overview of Catalyst as of version 13 is
-available in the talk [Catalyst.jl, Modeling Chemical Reaction Networks](https://www.youtube.com/watch?v=yreW94n98eM&ab_channel=TheJuliaProgrammingLanguage).
-- From JuliaCon 2022: A three hour tutorial workshop overviewing how to use
- Catalyst and its more advanced features as of version 12.1. [Workshop
- video](https://youtu.be/tVfxT09AtWQ), [Workshop Pluto.jl
- Notebooks](https://github.com/SciML/JuliaCon2022_Catalyst_Workshop).
-- From SIAM CSE 2021: A short 15 minute overview of Catalyst as of version 6 is
-available in the talk [Modeling Biochemical Systems with
-Catalyst.jl](https://www.youtube.com/watch?v=5p1PJE5A5Jw).
-- From JuliaCon 2018: A short 13 minute overview of Catalyst when it was known
- as DiffEqBiological in older versions is available in the talk [Efficient
- Modelling of Biochemical Reaction
- Networks](https://www.youtube.com/watch?v=s1e72k5XD6s)
-
-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).
+An overview of the package, its features, and comparative benchmarking (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).
## Features
-- A DSL provides a simple and readable format for manually specifying chemical
- reactions.
-- Catalyst `ReactionSystem`s provide a symbolic representation of reaction networks,
- built on [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/) and
- [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/).
-- Non-integer (e.g. `Float64`) stoichiometric coefficients are supported for generating
- ODE models, and symbolic expressions for stoichiometric coefficients are supported for
- all system types.
-- The [Catalyst.jl API](http://docs.sciml.ai/Catalyst/stable/api/catalyst_api) provides functionality for extending networks,
- building networks programmatically, network analysis, and for composing multiple
- networks together.
-- `ReactionSystem`s generated by the DSL can be converted to a variety of
- `ModelingToolkit.AbstractSystem`s, including symbolic ODE, SDE and jump process
- representations.
-- Coupled differential and algebraic constraint equations can be included in
- Catalyst models, and are incorporated during conversion to ODEs or steady
- state equations.
-- Conservation laws can be detected and applied to reduce system sizes, and generate
- non-singular Jacobians, during conversion to ODEs, SDEs, and steady state equations.
+#### Features of Catalyst
+- [The Catalyst DSL](https://docs.sciml.ai/Catalyst/stable/model_creation/dsl_basics/) provides a simple and readable format for manually specifying reaction
+ network models using chemical reaction notation.
+- Catalyst `ReactionSystem`s provides a symbolic representation of reaction networks,
+ built on [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/) and
+ [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/).
+- The [Catalyst.jl API](http://docs.sciml.ai/Catalyst/stable/api/catalyst_api) provides functionality
+ for extending networks, building networks programmatically, and for composing
+ multiple networks together.
+- Leveraging ModelingToolkit, generated models can be converted to symbolic reaction rate equation ODE models, symbolic Chemical Langevin Equation models, and symbolic stochastic chemical kinetics (jump process) models. These can be simulated using any
+ [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/)
+ [ODE/SDE/jump solver](https://docs.sciml.ai/Catalyst/stable/model_simulation/simulation_introduction/), and can be used within `EnsembleProblem`s for carrying
+ out [parallelized parameter sweeps and statistical sampling](https://docs.sciml.ai/Catalyst/stable/model_simulation/ensemble_simulations/). Plot recipes
+ are available for [visualization of all solutions](https://docs.sciml.ai/Catalyst/stable/model_simulation/simulation_plotting/).
+- Non-integer (e.g. `Float64`) stoichiometric coefficients [are supported](https://docs.sciml.ai/Catalyst/stable/model_creation/dsl_basics/#dsl_description_stoichiometries_decimal) for generating
+ ODE models, and symbolic expressions for stoichiometric coefficients [are supported](https://docs.sciml.ai/Catalyst/stable/model_creation/parametric_stoichiometry/) for
+ all system types.
+- A [network analysis suite](https://docs.sciml.ai/Catalyst/stable/model_creation/network_analysis/) permits the computation of linkage classes, deficiencies, reversibility, and other network properties.
+- [Conservation laws can be detected and utilized](https://docs.sciml.ai/Catalyst/stable/model_creation/network_analysis/#network_analysis_deficiency) to reduce system sizes, and to generate
+ non-singular Jacobians (e.g. during conversion to ODEs, SDEs, and steady state equations).
+- Catalyst reaction network models can be [coupled with differential and algebraic equations](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/)
+ (which are then incorporated during conversion to ODEs, SDEs, and steady state equations).
+- Models can be [coupled with events](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/#constraint_equations_events) that affect the system and its state during simulations.
- By leveraging ModelingToolkit, users have a variety of options for generating
- optimized system representations to use in solvers. These include construction
- of dense or sparse Jacobians, multithreading or parallelization of generated
- derivative functions, automatic classification of reactions into optimized
- jump types for Gillespie type simulations, automatic construction of
- dependency graphs for jump systems, and more.
-- Generated systems can be solved using any
- [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/)
- ODE/SDE/jump solver, and can be used within `EnsembleProblem`s for carrying
- out parallelized parameter sweeps and statistical sampling. Plot recipes
- are available for visualizing the solutions.
+ optimized system representations to use in solvers. These include construction
+ of [dense or sparse Jacobians](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated
+ derivative functions](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized
+ jump types for Gillespie type simulations](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#jump_types), [automatic construction of
+ dependency graphs for jump systems](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Jump-Aggregators-Requiring-Dependency-Graphs), and more.
- [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) symbolic
- expressions and Julia `Expr`s can be obtained for all rate laws and functions
- determining the deterministic and stochastic terms within resulting ODE, SDE
- or jump models.
-- [Latexify](https://korsbo.github.io/Latexify.jl/stable/) can be used to generate
- LaTeX expressions corresponding to generated mathematical models or the
- underlying set of reactions.
-- [Graphviz](https://graphviz.org/) can be used to generate and visualize
- reaction network graphs. (Reusing the Graphviz interface created in
- [Catlab.jl](https://algebraicjulia.github.io/Catlab.jl/stable/).)
-
-## Packages supporting Catalyst
-- Catalyst [`ReactionSystem`](@ref)s can be imported from SBML files via
- [SBMLToolkit.jl](https://github.com/SciML/SBMLToolkit.jl), and from BioNetGen .net
- files and various stoichiometric matrix network representations using
- [ReactionNetworkImporters.jl](https://github.com/SciML/ReactionNetworkImporters.jl).
-- [MomentClosure.jl](https://github.com/augustinas1/MomentClosure.jl) allows
- generation of symbolic ModelingToolkit `ODESystem`s, representing moment
- closure approximations to moments of the Chemical Master Equation, from
- reaction networks defined in Catalyst.
+ expressions and Julia `Expr`s can be obtained for all rate laws and functions determining the
+ deterministic and stochastic terms within resulting ODE, SDE or jump models.
+- [Steady states](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/homotopy_continuation/) (and their [stabilities](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/steady_state_stability_computation/)) can be computed for model ODE representations.
+
+#### Features of Catalyst composing with other packages
+- [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) Can be used to numerically solver generated reaction rate equation ODE models.
+- [StochasticDiffEq.jl](https://github.com/SciML/StochasticDiffEq.jl) can be used to numerically solve generated Chemical Langevin Equation SDE models.
+- [JumpProcesses.jl](https://github.com/SciML/JumpProcesses.jl) can be used to numerically sample generated Stochastic Chemical Kinetics Jump Process models.
+- Support for [parallelization of all simulations](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation), including parallelization of
+ [ODE simulations on GPUs](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation_GPU) using
+ [DiffEqGPU.jl](https://github.com/SciML/DiffEqGPU.jl).
+- [Latexify](https://korsbo.github.io/Latexify.jl/stable/) can be used to [generate LaTeX
+ expressions](https://docs.sciml.ai/Catalyst/stable/model_creation/model_visualisation/#visualisation_latex) corresponding to generated mathematical models or the
+ underlying set of reactions.
+- [Graphviz](https://graphviz.org/) can be used to generate and [visualize reaction network graphs](https://docs.sciml.ai/Catalyst/stable/model_creation/model_visualisation/#visualisation_graphs)
+ (reusing the Graphviz interface created in [Catlab.jl](https://algebraicjulia.github.io/Catlab.jl/stable/)).
+- Model steady states can be computed through homotopy continuation using [HomotopyContinuation.jl](https://github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl)
+ (which can find *all* steady states of systems with multiple ones), by forward ODE simulations using
+ [SteadyStateDiffEq.jl)](https://github.com/SciML/SteadyStateDiffEq.jl), or by numerically solving steady-state nonlinear equations using [NonlinearSolve.jl](https://github.com/SciML/NonlinearSolve.jl).
+- [BifurcationKit.jl](https://github.com/bifurcationkit/BifurcationKit.jl) can be used to [compute
+ bifurcation diagrams](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/bifurcation_diagrams/) of models' steady states (including finding periodic orbits).
+- [DynamicalSystems.jl](https://github.com/JuliaDynamics/DynamicalSystems.jl) can be used to compute
+ model [basins of attraction](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_basins_of_attraction) and [Lyapunov spectrums](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_lyapunov_exponents).
+- [StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) can be used
+ to [perform structural identifiability analysis](https://docs.sciml.ai/Catalyst/stable/inverse_problems/structural_identifiability/).
+- [Optimization.jl](https://github.com/SciML/Optimization.jl), [DiffEqParamEstim.jl](https://github.com/SciML/DiffEqParamEstim.jl),
+ and [PEtab.jl](https://github.com/sebapersson/PEtab.jl) can all be used to [fit model parameters to data](https://sebapersson.github.io/PEtab.jl/stable/Define_in_julia/).
+- [GlobalSensitivity.jl](https://github.com/SciML/GlobalSensitivity.jl) can be used to perform
+ [global sensitivity analysis](https://docs.sciml.ai/Catalyst/stable/inverse_problems/global_sensitivity_analysis/) of model behaviors.
+- [SciMLSensitivity.jl](https://github.com/SciML/SciMLSensitivity.jl) can be used to compute local sensitivities of functions containing forward model simulations.
+
+#### Features of packages built upon Catalyst
+- Catalyst [`ReactionSystem`](@ref)s can be [imported from SBML files](https://docs.sciml.ai/Catalyst/stable/model_creation/model_file_loading_and_export/#Loading-SBML-files-using-SBMLImporter.jl-and-SBMLToolkit.jl) via
+ [SBMLImporter.jl](https://github.com/SciML/SBMLImporter.jl) and [SBMLToolkit.jl](https://github.com/SciML/SBMLToolkit.jl),
+ and [from BioNetGen .net files](https://docs.sciml.ai/Catalyst/stable/model_creation/model_file_loading_and_export/#file_loading_rni_net) and various stoichiometric matrix network representations
+ using [ReactionNetworkImporters.jl](https://github.com/SciML/ReactionNetworkImporters.jl).
+- [MomentClosure.jl](https://github.com/augustinas1/MomentClosure.jl) allows generation of symbolic
+ ModelingToolkit `ODESystem`s that represent moment closure approximations to moments of the
+ Chemical Master Equation, from reaction networks defined in Catalyst.
- [FiniteStateProjection.jl](https://github.com/kaandocal/FiniteStateProjection.jl)
- allows the construction and numerical solution of Chemical Master Equation
- models from reaction networks defined in Catalyst.
+ allows the construction and numerical solution of Chemical Master Equation
+ models from reaction networks defined in Catalyst.
- [DelaySSAToolkit.jl](https://github.com/palmtree2013/DelaySSAToolkit.jl) can
- augment Catalyst reaction network models with delays, and can simulate the
- resulting stochastic chemical kinetics with delays models.
-- [BondGraphs.jl](https://github.com/jedforrest/BondGraphs.jl) a package for
- constructing and analyzing bond graphs models, which can take Catalyst models as input.
-- [PEtab.jl](https://github.com/sebapersson/PEtab.jl) a package that implements the PEtab format for fitting reaction network ODEs to data. Input can be provided either as SBML files or as Catalyst `ReactionSystem`s.
+ augment Catalyst reaction network models with delays, and can simulate the
+ resulting stochastic chemical kinetics with delays models.
+- [BondGraphs.jl](https://github.com/jedforrest/BondGraphs.jl), a package for
+ constructing and analyzing bond graphs models, which can take Catalyst models as input.
-## Illustrative examples
-#### Gillespie simulations of Michaelis-Menten enzyme kinetics
+## Illustrative example
+
+#### Deterministic ODE simulation of Michaelis-Menten enzyme kinetics
+Here we show a simple example where a model is created using the Catalyst DSL, and then simulated as
+an ordinary differential equation.
```julia
-using Catalyst, Plots, JumpProcesses
-rs = @reaction_network begin
- c1, S + E --> SE
- c2, SE --> S + E
- c3, SE --> P + E
+# Fetch required packages.
+using Catalyst, OrdinaryDiffEq, Plots
+
+# Create model.
+model = @reaction_network begin
+ kB, S + E --> SE
+ kD, SE --> S + E
+ kP, SE --> P + E
end
-p = (:c1 => 0.00166, :c2 => 0.0001, :c3 => 0.1)
-tspan = (0., 100.)
-u0 = [:S => 301, :E => 100, :SE => 0, :P => 0]
-
-# solve JumpProblem
-dprob = DiscreteProblem(rs, u0, tspan, p)
-jprob = JumpProblem(rs, dprob, Direct())
-jsol = solve(jprob, SSAStepper())
-plot(jsol; lw = 2, title = "Gillespie: Michaelis-Menten Enzyme Kinetics")
+
+# Create an ODE that can be simulated.
+u0 = [:S => 50.0, :E => 10.0, :SE => 0.0, :P => 0.0]
+tspan = (0., 200.)
+ps = (:kB => 0.01, :kD => 0.1, :kP => 0.1)
+ode = ODEProblem(model, u0, tspan, ps)
+
+# Simulate ODE and plot results.
+sol = solve(ode)
+plot(sol; lw = 5)
```
+
-
+#### Stochastic jump simulations
+The same model can be used as input to other types of simulations. E.g. here we instead generate and simulate a stochastic chemical kinetics jump process model.
+```julia
+# Create and simulate a jump process (here using Gillespie's direct algorithm).
+# The initial conditions are now integers as we track exact populations for each species.
+using JumpProcesses
+u0_integers = [:S => 50, :E => 10, :SE => 0, :P => 0]
+dprob = DiscreteProblem(model, u0_integers, tspan, ps)
+jprob = JumpProblem(model, dprob, Direct())
+jump_sol = solve(jprob, SSAStepper())
+plot(jump_sol; lw = 2)
+```
+
-#### Adaptive time stepping SDEs for a birth-death process
+## Elaborate example
+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 (modeled as the cyclic sinusoid $kₐ*(sin(t)+1)$), which
+phosphorylates the growth factor (producing $Gᴾ$). When the cell reaches a critical volume ($Vₘ$)
+it undergoes cell division. First, we declare our model:
```julia
-using Catalyst, Plots, StochasticDiffEq
-rs = @reaction_network begin
- c1, X --> 2X
- c2, X --> 0
- c3, 0 --> X
+using Catalyst
+cell_model = @reaction_network begin
+ @parameters Vₘ g
+ @equations begin
+ D(V) ~ g*Gᴾ
+ end
+ @continuous_events begin
+ [V ~ Vₘ] => [V ~ V/2]
+ end
+ kₚ*(sin(t)+1)/V, G --> Gᴾ
+ kᵢ/V, Gᴾ --> G
end
-p = (:c1 => 1.0, :c2 => 2.0, :c3 => 50.)
-tspan = (0.,10.)
-u0 = [:X => 5.]
-sprob = SDEProblem(rs, u0, tspan, p)
-ssol = solve(sprob, LambaEM(), reltol=1e-3)
-plot(ssol; lw = 2, title = "Adaptive SDE: Birth-Death Process")
```
+We now study the system as a Chemical Langevin Dynamics SDE model, which can be generated as follows
+```julia
+u0 = [:V => 25.0, :G => 50.0, :Gᴾ => 0.0]
+tspan = (0.0, 20.0)
+ps = [:Vₘ => 50.0, :g => 0.3, :kₚ => 100.0, :kᵢ => 60.0]
+sprob = SDEProblem(cell_model, u0, tspan, ps)
+```
+This produces the following equations:
+```math
+\begin{align*}
+dG(t) &= - \left( \frac{kₚ*(sin(t)+1)}{V(t)} G(t) + \frac{kᵢ}{V(t)} Gᴾ(t) \right) dt - \sqrt{\frac{kₚ*(sin(t)+1)}{V(t)} G(t)} dW_1(t) + \sqrt{\frac{kᵢ}{V(t)} Gᴾ(t)} dW_2(t) &
+dGᴾ(t) &= \left( \frac{kₚ*(sin(t)+1)}{V(t)} G(t) - \frac{kᵢ}{V(t)} Gᴾ(t) \right) dt + \sqrt{\frac{kₚ*(sin(t)+1)}{V(t)} G(t)} dW_1(t) - \sqrt{\frac{kᵢ}{V(t)} Gᴾ(t)} dW_2(t) &
+dV(t) &= \left(g \cdot Gᴾ(t)\right) dt
+\end{align*}
+```
+where the $dW_1(t)$ and $dW_2(t)$ terms represent independent Brownian Motions, encoding the noise added by the Chemical Langevin Equation. Finally, we can simulate and plot the results.
+```julia
+using StochasticDiffEq, Plots
+sol = solve(sprob, EM(); dt = 0.05)
+plot(sol; xguide = "Time (au)", lw = 2)
+```
+
-
-
-## Getting help
-Catalyst developers are active on the [Julia
-Discourse](https://discourse.julialang.org/), the [Julia Slack](https://julialang.slack.com) channels \#sciml-bridged and \#sciml-sysbio, and the [Julia Zulip sciml-bridged channel](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged).
-For bugs or feature requests [open an issue](https://github.com/SciML/Catalyst.jl/issues).
+Some features we used here:
+- The cell volume was [modeled as a differential equation, which was coupled to the reaction network model](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/#constraint_equations_coupling_constraints).
+- The cell divisions were created by [incorporating events into the model](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/#constraint_equations_events).
+- We designated a specific numeric [solver and corresponding solver options](https://docs.sciml.ai/Catalyst/stable/model_simulation/simulation_introduction/#simulation_intro_solver_options).
+- The model simulation was [plotted using Plots.jl](https://docs.sciml.ai/Catalyst/stable/model_simulation/simulation_plotting/).
+## Getting help or getting involved
+Catalyst developers are active on the [Julia Discourse](https://discourse.julialang.org/),
+the [Julia Slack](https://julialang.slack.com) channels \#sciml-bridged and \#sciml-sysbio, and the
+[Julia Zulip sciml-bridged channel](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged).
+For bugs or feature requests, [open an issue](https://github.com/SciML/Catalyst.jl/issues).
## Supporting and citing Catalyst.jl
-The software in this ecosystem was developed as part of academic research. If you would like to help support it,
-please star the repository as such metrics may help us secure funding in the future. If you use Catalyst as part
-of your research, teaching, or other activities, we would be grateful if you could cite our work:
+The software in this ecosystem was developed as part of academic research. If you would like to help
+support it, please star the repository as such metrics may help us secure funding in the future. If
+you use Catalyst as part of your research, teaching, or other activities, we would be grateful if you
+could cite our work:
```
@article{CatalystPLOSCompBio2023,
- doi = {10.1371/journal.pcbi.1011530},
- author = {Loman, Torkel E. AND Ma, Yingbo AND Ilin, Vasily AND Gowda, Shashi AND Korsbo, Niklas AND Yewale, Nikhil AND Rackauckas, Chris AND Isaacson, Samuel A.},
- journal = {PLOS Computational Biology},
- publisher = {Public Library of Science},
- title = {Catalyst: Fast and flexible modeling of reaction networks},
- year = {2023},
- month = {10},
- volume = {19},
- url = {https://doi.org/10.1371/journal.pcbi.1011530},
- pages = {1-19},
- number = {10},
+ doi = {10.1371/journal.pcbi.1011530},
+ author = {Loman, Torkel E. AND Ma, Yingbo AND Ilin, Vasily AND Gowda, Shashi AND Korsbo, Niklas AND Yewale, Nikhil AND Rackauckas, Chris AND Isaacson, Samuel A.},
+ journal = {PLOS Computational Biology},
+ publisher = {Public Library of Science},
+ title = {Catalyst: Fast and flexible modeling of reaction networks},
+ year = {2023},
+ month = {10},
+ volume = {19},
+ url = {https://doi.org/10.1371/journal.pcbi.1011530},
+ pages = {1-19},
+ number = {10},
}
-```
+```
\ No newline at end of file
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