Note for pre-version 5 users: Version 5 is a breaking release, with the DSL
now generating ModelingToolkit.ReactionSystem
s and DiffEqBiological being
renamed to Catalyst. As such, the @reaction_network
macro no longer allows
the generation of custom types. Please see the updated documentation to
understand changes to the API and functionality. In particular, the earlier
bifurcation functionality has not yet been updated to the new system. If you
rely on this functionality please do not update at this time, or consider using
BifurcationKit.jl.
Catalyst.jl is a domain specific language (DSL) for high performance simulation
and modeling of chemical reaction networks. Catalyst utilizes
ModelingToolkit
ReactionSystem
s, leveraging ModelingToolkit to enable large-scale simulations
through auto-vectorization and parallelism. 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 ecosystem,
including higher level SciML packages (e.g. for sensitivity analysis, parameter
estimation, machine learning applications, etc).
For information on using the package, see the stable documentation. The in-development documentation describes unreleased features in the current master branch.
- DSL provides a simple and readable format for manually specifying chemical reactions.
- The Catalyst.jl API provides functionality for extending networks, building networks programmatically, and for composing multiple networks together.
ReactionSystem
s generated by the DSL can be converted to a variety ofModelingToolkit.AbstractSystem
s, including ODE, SDE and jump process representations.- 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
ODE/SDE/jump solver, and can be used within
EnsembleProblem
s for carrying out GPU-parallelized parameter sweeps and statistical sampling. Plot recipes are available for visualizing the solutions. - 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 can be used to generate LaTeX expressions corresponding to generated mathematical models or the underlying set of reactions.
- Graphviz can be used through Catlab.jl to generate and visualize reaction network graphs.
rs = @reaction_network begin
c1, S + E --> SE
c2, SE --> S + E
c3, SE --> P + E
end c1 c2 c3
p = (0.00166,0.0001,0.1) # [c1,c2,c3]
tspan = (0., 100.)
u0 = [301., 100., 0., 0.] # [S,E,SE,P]
# 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")
using Catalyst, Plots, StochasticDiffEq, DiffEqJump
rs = @reaction_network begin
c1, X --> 2X
c2, X --> 0
c3, 0 --> X
end c1 c2 c3
p = (1.0,2.0,50.) # [c1,c2,c3]
tspan = (0.,10.)
u0 = [5.] # [X]
sprob = SDEProblem(rs, u0, tspan, p)
ssol = solve(sprob, LambaEM(), reltol=1e-3)
plot(ssol,lw=2,title="Adaptive SDE: Birth-Death Process")