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Breaking updates and feature summaries across releases

Catalyst unreleased (master branch)

  • Due to changes in upstream packages, the structural identifiability extension is currently broken.

Catalyst 14.4.1

  • Support for user-defined functions on the RHS when providing coupled equations for CRNs using the @equations macro. For example, the following now works: 
    using Catalyst
f(A, t) = 2*A*t
rn = @reaction_network begin
  @equations D(A) ~ f(A,t)
end
    Note that user-defined functions will not work on the LHS of equations.

Catalyst 14.4

  • Symbolics 6 support.

Catalyst 14.3

  • Support for simulating stochastic chemical kinetics models with explicitly time-dependent propensities (i.e. where the resulting JumpSystem contains VariableRateJumps). As such JumpProblems need to be defined over ODEProblems or SDEProblems instead of DiscreteProblems we have introduced a new input struct, JumpInputs, that handles selecting via analysis of the generated JumpSystem, i.e. one can now say 
    using Catalyst, OrdinaryDiffEq, JumpProcesses, Plots
rn = @reaction_network begin
    k*(1 + sin(t)), 0 --> A
end
jinput = JumpInputs(rn, [:A => 0], (0.0, 10.0), [:k => .5])
# note that jinput.prob isa ODEProblem
jprob = JumpProblem(jinput)
sol = solve(jprob, Tsit5())
plot(sol, idxs = :A)

rn = @reaction_network begin
    k, 0 --> A
end
jinput = JumpInputs(rn, [:A => 0], (0.0, 10.0), [:k => .5])
# note that jinput.prob isa DiscreteProblem
jprob = JumpProblem(jinput)
sol = solve(jprob)
plot(sol, idxs = :A)
    When calling solve for problems with explicit time-dependent propensities, i.e. where jinput.prob isa ODEProblem, note that one must currently explicitly select an ODE solver to handle time-stepping and integrating the time-dependent propensities.
  • Note that solutions to jump problems with explicit time-dependent propensities, i.e. a JumpProblem over an ODEProblem, require manual selection of the variables to plot. That is, currently plot(sol) will error in this case due to limitations in the SciMLBase plot recipe.

Catalyst 14.2

  • Support for auto-algorithm selection in JumpProblems. For systems with only propensities that do not have an explicit time-dependence (i.e. that are not VariableRateJumps in JumpProcesses), one can now run model simulations via 
    using Catalyst, JumpProcesses
model = @reaction_network begin
  kB, S + E --> SE
  kD, SE --> S + E
  kP, SE --> P + E
end
u0 = [:S => 50, :E => 10, :SE => 0, :P => 0]
tspan = (0., 200.)
ps = [:kB => 0.01, :kD => 0.1, :kP => 0.1]
dprob = DiscreteProblem(model, u0, tspan, ps)
jprob = JumpProblem(model, dprob)
sol = solve(jprob)
    For small systems this will just use Gillespie's Direct method, transitioning to using RSSA and RSSACR as system size increase. Once can still manually select a given SSA, but no longer needs to specify SSAStepper when calling solve, i.e. 
    # use the SortingDirect method instead
jprob = JumpProblem(model, dprob, SortingDirect())
sol = solve(jprob)
  • Latexify recipe improvements including display fixes for array symbolics.
  • Deficiency one and concentration robustness checks.

Catalyst 14.1.1

The expansion of ReactionSystem models to spatial lattices has been enabled. Here follows a simple example where a Brusselator model is expanded to a 20x20 grid of compartments, with diffusion for species X, and then simulated using ODEs. Finally, an animation of the simulation is created.

using Catalyst, CairoMakie, OrdinaryDiffEq

# Create `LatticeReactionSystem` model.
brusselator = @reaction_network begin
    A, ∅ --> X
    1, 2X + Y --> 3X
    B, X --> Y
    1, X --> ∅
end
diffusion_rx = @transport_reaction D X
lattice = CartesianGrid((20,20))
lrs = LatticeReactionSystem(brusselator, [diffusion_rx], lattice)

# Create a spatial `ODEProblem`.
u0 = [:X => rand(20, 20), :Y => 10.0]
tspan = (0.0, 40.0)
ps = [:A => 1.0, :B => 4.0, :D => 0.2]
oprob = ODEProblem(lrs, u0, tspan, ps)

# Simulate the ODE and plot the results.
sol = solve(oprob, FBDF())
lattice_animation(sol, :X, lrs, "brusselator.mp4")

The addition of spatial modelling in Catalyst contains a large number of new features, all of which are described in the corresponding documentation.

Catalyst 14.0.1

Bug fix to address that independent variables, like time, should now be @parameters according to MTKv9. Converted internal time variables to consistently use default_t() to hopefully avoid such issues going forward.

Catalyst 14.0

Breaking changes

Catalyst v14 was prompted by the (breaking) release of ModelingToolkit v9, which introduced several breaking changes to Catalyst. A summary of these (and how to handle them) can be found here. These are briefly summarised in the following bullet points:

  • ReactionSystems must now be marked complete before they are exposed to most forms of simulation and analysis. With the exception of ReactionSystems created through the @reaction_network macro, all ReactionSystems are not marked complete upon construction. The complete function can be used to mark ReactionSystems as complete. To construct a ReactionSystem that is not marked complete via the DSL the new @network_component macro can be used.
  • The states function has been replaced with unknowns. The get_states function has been replaced with get_unknowns.
  • Support for most units (with the exception of s, m, kg, A, K, mol, and cd) has currently been dropped by ModelingToolkit, and hence they are unavailable via Catalyst too. Its is expected that eventually support for relevant chemical units such as molar will return to ModelingToolkit (and should then immediately work in Catalyst too).
  • Problem parameter values are now accessed through prob.ps[p] (rather than prob[p]).
  • ModelingToolkit currently does not support the safe application of the remake function, or safe direct mutation, for problems for which remove_conserved = true was used when updating the values of initial conditions. Instead, the values of each conserved constant must be directly specified.
  • The reactionparams, numreactionparams, and reactionparamsmap functions have been deprecated and removed.
  • To be more consistent with ModelingToolkit's immutability requirement for systems, we have removed API functions that mutate ReactionSystems such as addparam!, addreaction!, addspecies, @add_reactions, and merge!. Please use ModelingToolkit.extend and ModelingToolkit.compose to generate new merged and/or composed ReactionSystems from multiple component systems.

General changes

  • default_t() and default_time_deriv() functions should be used for creating the default time independent variable and its differential. i.e. 
    # do
t = default_t()
@species A(t)

# avoid
@variables t
@species A(t)
  • It is now possible to add metadata to individual reactions, e.g. using: 
    rn = @reaction_network begin
    @parameters η
    k, 2X --> X2, [description="Dimerisation"]
end
getdescription(rn)
    a more detailed description can be found here.
  • SDEProblem no longer takes the noise_scaling argument. Noise scaling is now handled through the noise_scaling metadata (described in more detail here)
  • Fields of the internal Reaction structure have been changed. ReactionSystemss saved using serialize on previous Catalyst versions cannot be loaded using this (or later) versions.
  • A new function, save_reactionsystem, which permits the writing of ReactionSystem models to files, has been created. A thorough description of this function can be found here
  • Updated how compounds are created. E.g. use 
    @variables t C(t) O(t)
@compound CO2 ~ C + 2O
    to create a compound species CO2 that consists of C and two O.
  • Added documentation for chemistry-related functionality (compound creation and reaction balancing).
  • Added function isautonomous to check if a ReactionSystem is autonomous.
  • Added function steady_state_stability to compute stability for steady states. Example: 
    # Creates model.
rn = @reaction_network begin
    (p,d), 0 <--> X
end
p = [:p => 1.0, :d => 0.5]

# Finds (the trivial) steady state, and computes stability.
steady_state = [2.0]
steady_state_stability(steady_state, rn, p)
    Here, steady_state_stability takes an optional keyword argument tol = 10*sqrt(eps()), which is used to check that the real part of all eigenvalues are at least tol away from zero. Eigenvalues within tol of zero indicate that stability may not be reliably calculated.
  • Added a DSL option, @combinatoric_ratelaws, which can be used to toggle whether to use combinatorial rate laws within the DSL (this feature was already supported for programmatic modelling). Example: 
    # Creates model.
rn = @reaction_network begin
    @combinatoric_ratelaws false
    (kB,kD), 2X <--> X2
end
  • Added a DSL option, @observables for creating observables (this feature was already supported for programmatic modelling).
  • Added DSL options @continuous_events and @discrete_events to add events to a model as part of its creation (this feature was already supported for programmatic modelling). Example: 
    rn = @reaction_network begin
    @continuous_events begin
        [X ~ 1.0] => [X ~ X + 1.0]
    end
    d, X --> 0
end
  • Added DSL option @equations to add (algebraic or differential) equations to a model as part of its creation (this feature was already supported for programmatic modelling). Example: 
    rn = @reaction_network begin
    @equations begin
        D(V) ~ 1 - V
    end
    (p/V,d/V), 0 <--> X
end
    couples the ODE $dV/dt = 1 - V$ to the reaction system.
  • Coupled reaction networks and differential equation (or algebraic differential equation) systems can now be converted to SDESystems and NonlinearSystems.

Structural identifiability extension

  • Added CatalystStructuralIdentifiabilityExtension, which permits StructuralIdentifiability.jl to be applied directly to Catalyst systems. E.g. use 
    using Catalyst, StructuralIdentifiability
goodwind_oscillator = @reaction_network begin
    (mmr(P,pₘ,1), dₘ), 0 <--> M
    (pₑ*M,dₑ), 0 <--> E
    (pₚ*E,dₚ), 0 <--> P
end
assess_identifiability(goodwind_oscillator; measured_quantities=[:M])
    to assess (global) structural identifiability for all parameters and variables of the goodwind_oscillator model (under the presumption that we can measure M only).
  • Automatically handles conservation laws for structural identifiability problems (eliminates these internally to speed up computations).
  • A more detailed of how this extension works can be found here.

Bifurcation analysis extension

  • Add a CatalystBifurcationKitExtension, permitting BifurcationKit's BifurcationProblems to be created from Catalyst reaction networks. Example usage: 
    using Catalyst
wilhelm_2009_model = @reaction_network begin
    k1, Y --> 2X
    k2, 2X --> X + Y
    k3, X + Y --> Y
    k4, X --> 0
    k5, 0 --> X
end

using BifurcationKit
bif_par = :k1
u_guess = [:X => 5.0, :Y => 2.0]
p_start = [:k1 => 4.0, :k2 => 1.0, :k3 => 1.0, :k4 => 1.5, :k5 => 1.25]
plot_var = :X
bprob = BifurcationProblem(wilhelm_2009_model, u_guess, p_start, bif_par; plot_var = plot_var)

p_span = (2.0, 20.0)
opts_br = ContinuationPar(p_min = p_span[1], p_max = p_span[2], max_steps = 1000)

bif_dia = bifurcationdiagram(bprob, PALC(), 2, (args...) -> opts_br; bothside = true)

using Plots
plot(bif_dia; xguide = "k1", guide = "X")
  • Automatically handles elimination of conservation laws for computing bifurcation diagrams.
  • Updated Bifurcation documentation with respect to this new feature.

Catalyst 13.5

  • Added a CatalystHomotopyContinuationExtension extension, which exports the hc_steady_state function if HomotopyContinuation is exported. hc_steady_state finds the steady states of a reaction system using the homotopy continuation method. This feature is only available for julia versions 1.9+. Example:
wilhelm_2009_model = @reaction_network begin
    k1, Y --> 2X
    k2, 2X --> X + Y
    k3, X + Y --> Y
    k4, X --> 0
end
ps = [:k1 => 8.0, :k2 => 2.0, :k3 => 1.0, :k4 => 1.5]
hc_steady_states(wilhelm_2009_model, ps)

Catalyst 13.4

  • Added the ability to create species that represent chemical compounds and know their constituents. For example, water can be created and queried as 
    @variables t
@species H(t) O(t)
@compound H2O(t) 2*H O
iscompound(H2O) == true
isspecies(H2O) == true   # compounds are also species, so can be used in reactions
isequal(components(H2O), [H, O])
coefficients(H2O) == [2, 1]
  • Added reaction balancing via the balance_reaction command, which returns a vector of balanced reaction versions, i.e. 
    @variables t
@species H(t) O(t) C(t)
@compound CH4(t) C 4H
@compound O2(t) 2O
@compound CO2(t) C 2O
@compound H2O(t) 2H O

# unbalanced reaction to balance
rx = Reaction(1.0, [CH4, O2], [CO2, H2O])

# calculate a balanced version, this returns a vector
# storing a single balanced version of the reaction in this case
brxs = balance_reaction(rx)

# what one would calculate by hand
balanced_rx = Reaction(1.0, [CH4, O2], [CO2, H2O], [1, 2], [1, 2])

# testing equality
@test isequal(balanced_rx, first(brxs))
  • Note that balancing works via calculating the nullspace of an associated integer matrix that stores in entry (i,j) a signed integer representing the number of times the i'th atom appears within the jth compound. The entry is positive for a substrate and negative for a product. One cannot balance a reaction involving compounds of compounds currently. A non-empty solution vector is returned if the reaction can be balanced in exactly one way with minimal coefficients while preserving the set of substrates and products, i.e. if the dimension of the nullspace is one. If the dimension is greater than one we return a Reaction for each nullspace basis vector, but note that they may currently interchange substrates and products (i.e. we do not solve for if there is a linear combination of them that preserves the set of substrates and products). An empty Reaction vector indicates it is not possible to balance the reaction.

Catalyst 13.2

  • Array parameters, species, and variables can be use in the DSL if explicitly declared with @parameters, @species, or @variables respectively, i.e. 
    rn = @reaction_network begin
    @parameters k[1:2] a
    @variables (V(t))[1:2] W(t)
    @species (X(t))[1:2] Y(t)
    k[1]*a+k[2], X[1] + V[1]*X[2] --> V[2]*W*Y + B*C
end

Catalyst 13.1

  • Non-species states can be declared in the DSL using @variables, and custom independent variables (instead of just t) using @ivs. For the latter, the first independent variable is always interpreted as the time variable, and all discovered species are created to be functions of all the ivs. For example in 
    rn = @reaction_network begin
    @ivs s x
    @variables A(s) B(x) C(s,x)
    @species D(s) E(x) F(s,x)
    k*C, A*D + B*E --> F + H
end
    s will be the time variable, H = H(s,x) will be made a function of s and x, and A(s), B(x), and C(s,x) will be non-species state variables.
  • Catalyst.isequal_ignore_names has been deprecated for isequivalent(rn1, rn2) to test equality of two networks and ignore their name. To include names in the equality check continue to use rn1 == rn2 or use isequivalent(rn1, rn2; ignorenames = false).

Catalyst 13.0

  • BREAKING: Parameters should no longer be listed at the end of the DSL macro, but are instead inferred from their position in the reaction statements or via explicit declarations in the DSL macro. By default, any symbol that appears as a substrate or product is a species, while any other is a parameter. That is, parameters are those that only appear within a rate expression and/or as a stoichiometric coefficient. E.g. what previously was

    using Catalyst
rn = @reaction_network begin
  p, 0 --> X
  d, X --> 0
end p d

    is now

    using Catalyst
rn = @reaction_network begin
  p, 0 --> X
  d, X --> 0
end

    More generally, in the reaction system

    rn = @reaction_network begin
    k*k1*A, A --> B
    k2, k1 + k*A --> B
end

    k and k2 are inferred as parameters by the preceding convention, while A, B and k1 are species.

  • Explicit control over which symbols are treated as parameters vs. species is available through the new DSL macros, @species and @parameters. These can be used to designate when something should be a species or parameter, overriding the default DSL assignments. This allows setting that a symbol which would by default be interpreted as a parameter should actually be a species (or vice-versa). E.g. in:

    using Catalyst
rn = @reaction_network begin
  @species X(t)
  k*X, 0 --> Y
end

    X and Y will be considered species, while k will be considered a parameter. These options take the same arguments as standalone the @species (i.e. ModelingToolkit.@variables) and ModelingToolkit.@parameters macros, and support default values and setting metadata. E.g you can set default values using:

    using Catalyst
rn = @reaction_network begin
  @species X(t)=1.0
  @parameters p=1.0 d=0.1
  p, 0 --> X
  d, X --> 0
end

    or designate a parameter as representing a constant species using metadata:

    using Catalyst
rn = @reaction_network begin
  @parameters Y [isconstantspecies=true]
  k, X + Y --> 0
end

  • BREAKING: A standalone @species macro was added and should be used in place of @variables when declaring symbolic chemical species, i.e.

    @parameters k
@variables t
@species A(t) B(t)
rx = Reaction(k, [A], [B])
@named rs = ReactionSystem([rx], t)

    This will no longer work as substrates and products must be species

    @parameters k
@variables t A(t) B(t)
rx = Reaction(k, [A], [B]) # errors as neither A or B are species
rx = Reaction(k, [A], nothing) # errors as A is not a species
rx = Reaction(k, nothing, [B]) # errors as B is not a species

# this works as the rate or stoichiometry can be non-species
@species C(t) D(t)
rx = Reaction(k*A, [C], [D], [2], [B])
@named rs = ReactionSystem([rx], t)

    @variables is now reserved for non-chemical species state variables (for example, arising from constraint equations). Internally, species are normal symbolic variables, but with added metadata to indicate they represent chemical species.

  • To check if a symbolic variable is a species one can use isspecies:

    @variables t
@species A(t)
@variables B(t)
isspecies(A) == true
isspecies(B) == false

  • BREAKING: Constraint subsystems and the associated keyword argument to ReactionSystem have been removed. Instead, one can simply add ODE or algebraic equations into the list of Reactions passed to a ReactionSystem. i.e. this should now work

    @parameters k α
@variables t V(t)
@species A(t)
rx = Reaction(k*V, nothing, [A])
D = Differential(t)
eq = D(V) ~ α
@named rs = ReactionSystem([rx, eq], t)
osys = convert(ODESystem, rs)

    which gives the ODE model

    julia> equations(osys)
2-element Vector{Equation}:
  Differential(t)(A(t)) ~ k*V(t)
  Differential(t)(V(t)) ~ α

    Mixing ODEs and algebraic equations is allowed and should work when converting to an ODESystem or NonlinearSystem (if only algebraic equations are included), but is not currently supported when converting to JumpSystems or SDESystems.

  • API functions applied to a ReactionSystem, rs, now have:

    • species(rs) give the chemical species of a system.
    • states(rs) give all the variables, both chemical species and non-chemical species of a system.

    Catalyst now orders species before non-species in states(rs) such that states(rs)[1:length(species(rs))] and species(rs) should be the same. Similarly:

    • equations(rs) gives the set of Reactions and Equations of a system.
    • reactions(rs) gives the Reactions of a system.

    As with species, Reactions are always ordered before Equations so that equations(rs)[1:length(reactions(rs))] should be the same ordered list of Reactions as given by reactions(rs).

  • Catalyst has been updated for Symbolics v5, and requires Symbolics v5.0.3 or greater and ModelingToolkit v8.47.0 or greater.

  • The accessors for a given system, rs, that return the internal arrays at the top-level (i.e. ignoring sub-systems) now have

    • ModelingToolkit.get_states(rs) to get the list of all species and non-species variables.
    • Catalyst.get_species(rs) to get the list of all species variables. Note that get_states(rs)[1:length(get_species(rs))] should be the same ordered list of species as get_species(rs).
    • ModelingToolkit.get_eqs(rs) gives the list of all Reactions and then Equations in the system.
    • Catalyst.get_rxs(rs) gives the list of all Reactions, such that get_eqs(rs)[1:length(get_rx(rs))] is the same ordered list of Reactions as returned by get_rxs(rs).

  • BREAKING: Chemical species specified or inferred via the DSL are now created via the same mechanism as @species, and therefore have the associated metadata that is missing from a normal symbolic variable.

  • Deprecated functions params and merge have been removed.

  • BREAKING: The old notation for the constants representing conserved quantities, _Conlaw, has been replaced with uppercase unicode gamma, "Γ". This can be entered in notebooks, the REPL, or many editors by typing the corresponding Latex command, "\Gamma", and hitting tab. This leads to much cleaner equations when Latexifying systems where conservation laws have been applied. The underlying symbol can also be accessed via Catalyst.CONSERVED_CONSTANT_SYMBOL.

  • Modelingtoolkit symbolic continuous and discrete events are now supported when creating ReactionSystems via the continuous_events and discrete_events keyword arguments. As in ModelingToolkit, species, states, and parameters that appear only within events are not detected automatically, and hence the four-argument ReactionSystem constructor, where states and parameters are explicitly passed, must be used unless every variable, state, or parameter in the events appears within a Reaction or Equation too. See the ModelingToolkit docs for more information on using events. Note that JumpSystems only support discrete events at this time.

Catalyst 12.3.2

  • Support for states/species that are functions of multiple variables. This enables (symbolically) building PDEs to solve with MethodOfLines. To use multiple independent variables one can say: 
    using Catalyst
using ModelingToolkit: scalarize
@parameters k[1:7]
@variables t x y U(x,y,t) V(x,y,t) W(x,y,t)
rxs = [Reaction(k[1], [U, W], [V, W]),
      Reaction(k[2], [V], [W], [2], [1]),
      Reaction(k[3], [W], [V], [1], [2]),
      Reaction(k[4], [U], nothing),
      Reaction(k[5], nothing, [U]),
      Reaction(k[6], [V], nothing),
      Reaction(k[7], nothing, [V])]
pars = scalarize(k)
@named rn = ReactionSystem(rxs, t, [U, V, W], pars; spatial_ivs = [x, y])
    The spatial_ivs keyword lets Catalyst know which independent variables correspond to spatial variables. Note that rate expressions can depend on x and y too, i.e. k[1] * x + y*t would be valid. See the work in progress PDE tutorial to solve the resulting system and add spatial transport.

Catalyst 12.3

  • API functions to generate substrate, product, and net stoichiometry matrices should now work with floating point stoichiometric coefficients. Note, symbolic coefficients are still not supported by such functions.

Catalyst 12.0

  • BREAKING: Modified how constant and boundary condition species (in the SBML sense) work. Constant species should now be specified as ModelingToolkit @parameters with the isconstantspecies=true metadata, while non-constant boundary condition species should be specified as ModelingToolkit @variables with the isbcspecies=true metadata. As before, boundary condition species are treated as constant with respect to reactions, but since they are considered variables their dynamics should be defined in a constraint system. Moreover, it is required that BC species appear in a balanced manner (i.e. in each reaction for which a BC species is a reactant it must appear as a substrate and a product with the same stoichiometry). Right now only conversion of ReactionSystems to an ODESystem with a constraint ODESystem or NonlinearSystem, or conversion to a NonlinearSystem with a constraint NonlinearSystem, are supported. Constraints are not supported in SDESystem or JumpSystem conversion, and so boundary condition species are effectively constant when converting to those model types (but still left as states instead of parameters). Defining constant and boundary condition species is done by 
    @parameters k A [isconstantspecies=true]
@variables t  B(t) [isbcspecies=true] C(t)
rx = Reaction(k, [A,B], [B,C], [1,2], [1,1])
    Here A is a constant species, B is a non-constant boundary condition species, and C is a normal species. Constant and boundary condition species can be used in creating Reactions like normal species as either substrates or products. Note that network API functions such as netstoichmat, conservationlaws, or reactioncomplexes ignore constant species. i.e. for A a constant species the reaction 2A + B --> C is treated as equivalent to B --> C with a modified rate constant, while B --> A would be identical to B --> 0. Boundary condition species are checked to be balanced by default when ReactionSystems are constructed, i.e. 
    rx = Reaction(k, [A,B], [C], [1,2], [1])
@named rs = ReactionSystem(rs, t)
    would error since B only appears as a substrate. This check can be disabled with 
    @named rs = ReactionSystem(rs, t; balanced_bc_check=false)
    Note that network analysis functions assume BC species appear in a balanced manner, so may not work correctly if one appears in an unbalanced fashion. (Conversion to other system types should still work just fine.)

Catalyst 11.0

  • BREAKING: Added the ability to eliminate conserved species when generating ODEs, nonlinear problems, SDEs, and steady state problems via the remove_conserved=true keyword that can be passed to convert or to ODEProblem, NonlinearProblem, SDEProblem, or SteadyStateProblem when called with a ReactionSystem. For example, 
    rn = @reaction_network begin
   k, A + B --> C
   k2, C --> A + B
   end k k2
osys = convert(ODESystem, rn; remove_conserved=true)
equations(osys)
    gives 
    Differential(t)(A(t)) ~ k2*(_ConLaw[2] - A(t)) - k*(A(t) + _ConLaw[1])*A(t)
    Initial conditions should still be specified for all the species in rn, and the conserved constants will then be calculated automatically. Eliminated species are stored as observables in osys and still accessible via solution objects. Breaking as this required modifications to the ReactionSystem type signature.
  • BREAKING: Added an internal cache in ReactionSystems for network properties, and revamped many of the network analysis functions to use this cache (so just a ReactionSystem can be passed in). Most of these functions will now only calculate the chosen property the first time they are called, and in subsequent calls will simply returned that cached value. Call reset_networkproperties! to clear the cache and allow properties to be recalculated. The new signatures for rn a ReactionSystem are 
    reactioncomplexmap(rn)
reactioncomplexes(rn)
complexstoichmat(rn)
complexoutgoingmat(rn)
incidencemat(rn)
incidencematgraph(rn)
linkageclasses(rn)
deficiency(rn)
sns = subnetworks(rn)
linkagedeficiencies(rn)
isreversible(rn)
isweaklyreversible(rn, sns)
    Breaking as this required modifications to the ReactionSystem type signature.
  • BREAKING ReactionSystems now store a default value for combinatoric_ratelaws=true. This default value can be set in the ReactionSystem constructor call as a keyword argument. Passing combinatoric_ratelaws as a keyword to convert or problem calls involving a ReactionSystem is still allowed, and will override the ReactionSystem's default.
  • Fixed a bug where ODESystem constraint systems did not propagate continuous_events during calls to convert(ODESystem, rn::ReactionSystem).
  • Added constant and boundary condition species (in the SBML sense). During conversion constant species are converted to parameters, while boundary condition species are kept as state variables. Note that boundary condition species are treated as constant with respect to reactions, so their dynamics must be defined in a constraint system. Right now only conversion of ReactionSystems to an ODESystem with a constraint ODESystem or NonlinearSystem, or conversion to a NonlinearSystem with a constraint NonlinearSystem, are supported. Constraints are not supported in SDESystem or JumpSystem conversion, and so boundary condition species are effectively constant when converting to those model types (but still left as states instead of parameters). Defining constant and boundary condition species is done by 
    @variables t A(t) [isconstant=true] B(t) [isbc=true] C(t)
    Here A is a constant species, B is a boundary condition species, and C is a normal species. Note that network API functions do not make use of these labels, and treat all species as normal -- these properties are only made use of when converting to other system types.

Catalyst 10.8

  • Added the ability to use symbolic stoichiometry expressions via the DSL. This should now work

    rn = @reaction_network rs begin
  t*k, (α+k+B)*A --> B
  1.0, α*A + 2*B --> k*C + α*D
end k α

    Here Catalyst will try to preserve the order of symbols within an expression, taking the rightmost as the species and everything multiplying that species as stoichiometry. For example, we can interpret the above reaction as S1 A --> S2 b where S1 = (α+k+B) is the stoichiometry of the reactant A and 1 is the stoichiometry of the reactant B. For

    rn = @reaction_network rs begin
  1.0, 2X*(Y + Z) --> XYZ
end

    all of X, Y and Z will be registered as species, with substrates (Y,Z) having associated stoichiometries of (2X,2X). As for rate expressions, any symbols that appear and are not defined as parameters will be declared to be species.

    In contrast, when declaring reactions

    rx = @reaction t*k, (k+α)*A --> B

    will work, with every symbol declared a parameter except the leftmost symbol in the reaction line. So

    rx = @reaction 1.0, 2X*(Y + Z) --> XYZ

    will make X a parameter and Y, Z and XYZ species.

  • Symbolic stoichiometry supports interpolation of expressions in @reaction_network and @reaction.

Catalyst 10.7

  • Added the ability to use symbolic variables, parameters and expressions for stoichiometric coefficients. See the new tutorial on Parametric Stoichiometry for details, and note the caveat about ModelingToolkit converting integer parameters to floating point types that must be worked around to avoid calls to factorial that involve floats.

Catalyst 10.6

  • Added the ability to use floating point stoichiometry (currently only tested for generating ODE models). This should now work 
    rn = @reaction_network begin
  k, 2.5*A --> 3*B
end k
    or directly 
    @parameters k b
@variables t A(t) B(t) C(t) D(t)
rx1 = Reaction(k,[B,C],[B,D], [2.5,1],[3.5, 2.5])
rx2 = Reaction(2*k, [B], [D], [1], [2.5])
rx3 = Reaction(2*k, [B], [D], [2.5], [2])
@named mixedsys = ReactionSystem([rx1,rx2,rx3],t,[A,B,C,D],[k,b])
osys = convert(ODESystem, mixedsys; combinatoric_ratelaws=false)
    Note, when using convert(ODESystem, mixedsys; combinatoric_ratelaws=false) the combinatoric_ratelaws=false parameter must be passed. This is also true when calling ODEProblem(mixedsys,...; combinatoric_ratelaws=false). This disables Catalyst's standard rescaling of reaction rates when generating reaction rate laws, see the docs. Leaving this out for systems with floating point stoichiometry will give an error message.

Catalyst 10.5

  • Added @reaction macro 
    rx = @reaction k*v, A + B --> C + D

# is equivalent to
@parameters k v
@variables t A(t) B(t) C(t) D(t)
rx == Reaction(k*v, [A,B], [C,D])
    Here k and v will be parameters and A, B, C and D will be variables. Interpolation of existing parameters/variables also works 
    @parameters k b
@variables t A(t)
ex = k*A^2 + t
rx = @reaction b*$ex*$A, $A --> C
    Any symbols arising in the rate expression that aren't interpolated are treated as parameters, while any in the reaction part (A + B --> C + D) are treated as species.

Catalyst 10.4

  • Added symmap_to_varmap, setdefaults!, and updated all *Problem(rn,...) calls to allow setting initial conditions and parameter values using symbol maps. See the Catalyst API for details. These allow using regular Julia Symbols to specify parameter values and initial conditions. i.e. to set defaults we can do 
    rn = @reaction_network begin
    α, S + I --> 2I
    β, I --> R
end α β
setdefaults!(rn, [:S => 999.0, :I => 1.0, :R => 0.0, :α => 1e-4, :β => .01])
op    = ODEProblem(rn, [], (0.0,250.0), [])
sol   = solve(op, Tsit5())
    To explicitly pass initial conditions and parameters using symbols we can do 
    rn = @reaction_network begin
    α, S + I --> 2I
    β, I --> R
end α β
u0 = [:S => 999.0, :I => 1.0, :R => 0.0]
p  = (:α => 1e-4, :β => .01)
op    = ODEProblem(rn, u0, (0.0,250.0), p)
sol   = solve(op, Tsit5())
    In each case ModelingToolkit symbolic variables can be used instead of Symbols, e.g. 
    @parameters α β
@variables t S(t) I(t) R(t)
setdefaults!(rn, [S => 999.0, I => 1.0, R => 0.0, α => 1e-4, β => .01])

Catalyst 10.3

  • BREAKING: The order of the parameters in the ReactionSystem's .ps field has been changed (only when created through the @reaction_network macro). Previously they were ordered according to the order with which they appeared in the macro. Now they are ordered according the to order with which they appeared after the end part. E.g. in 
    rn = @reaction_network begin
  (p,d), 0 <--> X
end d p
    previously the order was [p,d], while now it is [d, p].

Catalyst 10.1

  • Added support for @unpack observable_variable = rn and rn.observable_variable. This requires a new inner constructor definition for ReactionSystems, but is not considered breaking as the inner constructor is considered private.
  • Support added for ModelingToolkit 7 and Symbolics 4.

Catalyst 10.0

  • ReactionSystem(rxs::Vector{Reaction}, t) should now work and will infer the species and parameters.
  • BREAKING: Any undeclared variables in the DSL are now inferred to be species. i.e. this no longer errors, and B is assumed to be a species 
    rn = @reaction_network begin
  k*B, A --> C
end k
  • BREAKING: Internal changes mean the order of species or parameters in generated systems may have changed. Changes that induce different orders will not be considered breaking in the future.
  • Added interpolation in the DSL for species, variables, and the network name. i.e. this is now valid 
    @parameters k
@variables t, A(t)
spec = A
rate = k*A
name = :network
rn = @reaction_network $name begin
  $rate*B, 2*$spec + B --> $spec + C
  end
  • Added the ability to compose ReactionSystems via subsystems, and include either ODESystems or NonlinearSystems as subsystems. Note, if using non-ReactionSystem subsystems it is not currently possible to convert to a JumpSystem or SDESystem. It is also not possible to include either SDESystems or JumpSystems as subsystems.
  • Added extend(sys, reactionnetwork, name=nameof(sys)) to extend ReactionSystems with constraint equations (algebraic equations or ODEs), or other ReactionSystems. Algebraic or differential constraints are stored as a NonlinearSystem or ODESystem within the ReactionSystem, and accessible via get_constraints(reactionnetwork).
  • Added Catalyst.flatten(rn) to allow flattening of a ReactionSystem with sub-systems into one ReactionSystem. Non-ReactionSystem subsystems are merged into the constraints of the flattened ReactionSystem, and accessible via get_constraints.
  • BREAKING: ReactionSystems are now always flattened when calling convert. This should only affect models that use subsystems.
  • Added incidencematgraph, linkageclasses, deficiency, subnetworks, linkagedeficiency, isreversible and isweaklyreversible API functions.
  • Deprecated merge, use ModelingToolkit.extend instead.
  • Deprecated params and numparams (use ModelingToolkit.parameters to get all parameters of a system and all subsystems, or use reactionparams to get all parameters of a system and all ReactionSystem subsystems. The latter correspond to those parameters used within Reactions.)
  • BREAKING: Added a custom hash for Reactions to ensure they work in Dicts and Sets properly, ensuring set-type comparisons between collections of Reactions work.
  • Updated the docs and added a new tutorial on using compositional tooling.

Catalyst 9.0

1. BREAKING: netstoichmat, prodstoichmat and substoichmat are now transposed to be number of species by number of reactions. This is more consistent with the chemical reaction network literature for stoichiometry matrices.

2. reactioncomplexmap added to provide a mapping from reaction complexes to reactions they participate in.

3. Most API *mat functions now take an optional sparse keyword argument. If passed sparse=true a sparse matrix representation is generated, otherwise the default sparse=false value returns dense Matrix representations.

Catalyst 8.3

1. Network representations for the reaction complexes of a system along with associated graph functionality:

rn = @reaction_network begin
           k₁, 2A --> B
           k₂, A --> C
           k₃, C --> D
           k₄, B + D --> E
           k₅, B --> E
           k₆, D --> C
     end k₁ k₂ k₃ k₄ k₅ k₆
smap  = speciesmap(rn)
rcs,B = reactioncomplexes(rn; smap=smap)
Z     = complexstoichmat(rn; rcs=rcs)
Δ     = complexoutgoingmat(rn; B=B)
complexgraph(rn; complexdata=(rcs,B))

which gives

rn_complexes

2. Support for units via ModelingToolkit and Unitful.jl in directly constructed ReactionSystems:

# ]add Unitful
using Unitful
@parameters α [unit=u"μM/s"] β [unit=u"s"^(-1)] γ [unit=u"μM*s"^(-1)]
@variables t [unit=u"s"] A(t) [unit=u"μM"] B(t) [unit=u"μM"] C(t) [unit=u"μM"]
rxs = [Reaction(α, nothing, [A]),
       Reaction(β, [A], [B]),
       Reaction(γ, [A,B], [B], [1,1], [2])]
@named rs = ReactionSystem(rxs, t, [A,B,C], [α,β,γ])

By default, during construction of rs Catalyst will call

validate(rs)

which will print warnings and return false if either

  1. The species(rs) do not all have the same units.
  2. The implicit (ODE) rate laws for each reaction do not have units of (species units) / (time units), where the time units are the units of t.

(Note, at this time the @reaction_network macro does not support units.)

3. Calculation of conservation laws

rn = @reaction_network begin
  (k₊,k₋), A + B <--> C
  end k₊ k₋
clawmat = conservationlaws(netstoichmat(rn))

giving

 1  -1  0
 0   1  1

and

cquants = conservedquantities(species(rn), clawmat)

giving

 A(t) - B(t)
 B(t) + C(t)

See the API docs for more details about each of these new features.

Catalyst 8.2

1. Basic unit validation has been added following its addition for all ModelingToolkit systems.

Catalyst 8.1

1. reactioncomplexes, ReactionComplex, reactionrates, complexstoichmat and complexoutgoingmat are added to allow the calculation of reaction complex-based network matrix representations.

Catalyst 8.0

BREAKING: This is a breaking release, with all ModelingToolkit ReactionSystem and Reaction functionality migrated to Catalyst.

Catalyst 6.11

1. Plain text arrows "<--" and "<-->" for backward and reversible reactions are available if using Julia 1.6 or higher:

rn = @reaction_network begin
  (k1,k2), A + B <--> C
  k3, 0 <-- C
end k1 k2 k3

2. BREAKING: Reaction networks can be named

rn = @reaction_network Reversible_Reaction begin
  k1, A --> B
  k2, B --> A
  end k1 k2
ModelingToolkit.nameof(rn) == :Reversible_Reaction

Note, empty networks can no longer be created with parameters, i.e. only

rn = @reaction_network          # uses a randomly generated name
rn = @reaction_network MyName   # is named MyName

are allowed.

3. Compositional modeling with generated ODESystems, see here for an example that composes three gene modules to make the repressilator.