network analysis updates

Author: vyudu

.github/workflows/DocsCleanup.yml

@@ -0,0 +1,33 @@
+name: Doc Preview Cleanup
+
+on:
+  pull_request:
+    types: [closed]
+
+# Ensure that only one "Doc Preview Cleanup" workflow is force pushing at a time
+concurrency:
+  group: doc-preview-cleanup
+  cancel-in-progress: false
+
+jobs:
+  doc-preview-cleanup:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - name: Checkout gh-pages branch
+        uses: actions/checkout@v4
+        with:
+          ref: gh-pages
+      - name: Delete preview and history + push changes
+        run: |
+          if [ -d "${preview_dir}" ]; then
+              git config user.name "Documenter.jl"
+              git config user.email "[email protected]"
+              git rm -rf "${preview_dir}"
+              git commit -m "delete preview"
+              git branch gh-pages-new $(echo "delete history" | git commit-tree HEAD^{tree})
+              git push --force origin gh-pages-new:gh-pages
+          fi
+        env:
+          preview_dir: previews/PR${{ github.event.number }}

Project.toml

@@ -58,7 +58,7 @@ JumpProcesses = "9.13.2"
 LaTeXStrings = "1.3.0"
 Latexify = "0.16.5"
 MacroTools = "0.5.5"
-ModelingToolkit = "9.32"
+ModelingToolkit = "< 9.60"
 NetworkLayout = "0.4.7"
 Parameters = "0.12"
 Reexport = "0.2, 1.0"
@@ -67,7 +67,7 @@ RuntimeGeneratedFunctions = "0.5.12"
 SciMLBase = "2.57.2"
 Setfield = "1"
 # StructuralIdentifiability = "0.5.8"
-SymbolicUtils = "2.1.2, 3.3.0"
+SymbolicUtils = "3.8.1"
 Symbolics = "6.22"
 Unitful = "1.12.4"
 julia = "1.10"

docs/Project.toml

@@ -61,7 +61,7 @@ IncompleteLU = "0.2"
 JumpProcesses = "9.13.2"
 Latexify = "0.16.5"
 LinearSolve = "2.30"
-ModelingToolkit = "9.32"
+ModelingToolkit = "< 9.60"
 NonlinearSolve = "3.12, 4"
 Optim = "1.9"
 Optimization = "4"

src/Catalyst.jl

@@ -128,8 +128,8 @@ export @reaction_network, @network_component, @reaction, @species
 # Network analysis functionality.
 include("network_analysis.jl")
 export reactioncomplexmap, reactioncomplexes, incidencemat
-export complexstoichmat
-export complexoutgoingmat, incidencematgraph, linkageclasses, stronglinkageclasses,
+export complexstoichmat, laplacianmat, fluxmat, massactionvector, complexoutgoingmat
+export incidencematgraph, linkageclasses, stronglinkageclasses,
        terminallinkageclasses, deficiency, subnetworks
 export linkagedeficiencies, isreversible, isweaklyreversible
 export conservationlaws, conservedquantities, conservedequations, conservationlaw_constants

src/network_analysis.jl

@@ -192,6 +192,143 @@ function complexstoichmat(::Type{Matrix{Int}}, rn::ReactionSystem, rcs)
     Z
 end
 
+@doc raw"""
+    laplacianmat(rn::ReactionSystem, pmap=Dict(); sparse=false)
+
+    Return the negative of the graph Laplacian of the reaction network. The ODE system of a chemical reaction network can be factorized as ``\frac{dx}{dt} = Y A_k Φ(x)``, where ``Y`` is the [`complexstoichmat`](@ref) and ``A_k`` is the negative of the graph Laplacian, and ``Φ`` is the [`massactionvector`](@ref). ``A_k`` is an n-by-n matrix, where n is the number of complexes, where ``A_{ij} = k_{ij}`` if a reaction exists between the two complexes and 0 otherwise. 
+    Returns a symbolic matrix by default, but will return a numerical matrix if parameter values are specified via pmap. 
+
+    **Warning**: Unlike other Catalyst functions, the `laplacianmat` function will return a `Matrix{Num}` in the symbolic case. This is to allow easier computation of the matrix decomposition of the ODEs, and to ensure that multiplying the sparse form of the matrix will work.
+"""
+function laplacianmat(rn::ReactionSystem, pmap::Dict = Dict(); sparse = false)
+    D = incidencemat(rn; sparse)
+    K = fluxmat(rn, pmap; sparse)
+    D * K
+end
+
+Base.zero(::Type{Union{R, Symbolics.BasicSymbolic{Real}}}) where R <: Real = zero(R)
+Base.one(::Type{Union{R, Symbolics.BasicSymbolic{Real}}}) where R <: Real = one(R)
+
+@doc raw"""
+    fluxmat(rn::ReactionSystem, pmap = Dict(); sparse=false)
+
+    Return an r×c matrix ``K`` such that, if complex ``j`` is the substrate complex of reaction ``i``, then ``K_{ij} = k``, the rate constant for this reaction. Mostly a helper function for the network Laplacian, [`laplacianmat`](@ref). Has the useful property that ``\frac{dx}{dt} = S*K*Φ(x)``, where S is the [`netstoichmat`](@ref) or net stoichiometry matrix and ``Φ(x)`` is the [`massactionvector`](@ref).
+    Returns a symbolic matrix by default, but will return a numerical matrix if rate constants are specified as a `Tuple`, `Vector`, or `Dict` of symbol-value pairs via `pmap`.
+
+    **Warning**: Unlike other Catalyst functions, the `fluxmat` function will return a `Matrix{Num}` in the symbolic case. This is to allow easier computation of the matrix decomposition of the ODEs, and to ensure that multiplying the sparse form of the matrix will work.
+"""
+function fluxmat(rn::ReactionSystem, pmap::Dict = Dict(); sparse=false)
+    rates = if isempty(pmap)
+        reactionrates(rn)
+    else
+        substitutevals(rn, pmap, parameters(rn), reactionrates(rn))
+    end
+
+    rcmap = reactioncomplexmap(rn)
+    nc = length(rcmap)
+    nr = length(rates)
+    mtype = eltype(rates) <: Symbolics.BasicSymbolic ? Num : eltype(rates)
+    if sparse
+        return fluxmat(SparseMatrixCSC{mtype, Int}, rcmap, rates)
+    else
+        return fluxmat(Matrix{mtype}, rcmap, rates)
+    end
+end
+
+function fluxmat(::Type{SparseMatrixCSC{T, Int}}, rcmap, rates) where T
+    Is = Int[]
+    Js = Int[]
+    Vs = T[]
+    for (i, (complex, rxs)) in enumerate(rcmap)
+        for (rx, dir) in rxs
+            dir == -1 && begin
+                push!(Is, rx)
+                push!(Js, i)
+                push!(Vs, rates[rx])
+            end
+        end
+    end
+    Z = sparse(Is, Js, Vs, length(rates), length(rcmap))
+end
+
+function fluxmat(::Type{Matrix{T}}, rcmap, rates) where T
+    nr = length(rates)
+    nc = length(rcmap)
+    K = zeros(T, nr, nc)
+    for (i, (complex, rxs)) in enumerate(rcmap)
+        for (rx, dir) in rxs 
+            dir == -1 && (K[rx, i] = rates[rx])
+        end
+    end
+    K
+end
+
+function fluxmat(rn::ReactionSystem, pmap::Vector) 
+    pdict = Dict(pmap)
+    fluxmat(rn, pdict)
+end
+
+function fluxmat(rn::ReactionSystem, pmap::Tuple) 
+    pdict = Dict(pmap)
+    fluxmat(rn, pdict)
+end
+
+# Helper to substitute values into a (vector of) symbolic expressions. The syms are the symbols to substitute and the symexprs are the expressions to substitute into.
+function substitutevals(rn::ReactionSystem, map::Dict, syms, symexprs)
+    length(map) != length(syms) && error("Incorrect number of parameter-value pairs were specified.")
+    map = symmap_to_varmap(rn, map)
+    map = Dict(ModelingToolkit.value(k) => v for (k, v) in map)
+    vals = [substitute(expr, map) for expr in symexprs]
+end
+
+"""
+    massactionvector(rn::ReactionSystem, scmap = Dict(); combinatoric_ratelaws = true)
+
+    Return the vector whose entries correspond to the "mass action products" of each complex. For example, given the complex A + B, the corresponding entry of the vector would be ``A*B``, and for the complex 2X + Y, the corresponding entry would be ``X^2*Y``. The ODE system of a chemical reaction network can be factorized as ``\frac{dx}{dt} = Y A_k Φ(x)``, where ``Y`` is the [`complexstoichmat`](@ref) and ``A_k`` is the negative of the [`laplacianmat`](@ref). This utility returns ``Φ(x)``.
+    Returns a symbolic vector by default, but will return a numerical vector if species concentrations are specified as a tuple, vector, or dictionary via scmap.
+    If the `combinatoric_ratelaws` option is set, will include prefactors for that (see [introduction to Catalyst's rate laws](@ref introduction_to_catalyst_ratelaws). Will default to the default for the system.
+
+    **Warning**: Unlike other Catalyst functions, the `massactionvector` function will return a `Vector{Num}` in the symbolic case. This is to allow easier computation of the matrix decomposition of the ODEs.
+"""
+function massactionvector(rn::ReactionSystem, scmap::Dict = Dict(); combinatoric_ratelaws = Catalyst.get_combinatoric_ratelaws(rn))
+    r = numreactions(rn)
+    rxs = reactions(rn)
+    sm = speciesmap(rn)
+
+    specs = if isempty(scmap) 
+        species(rn)
+    else
+        substitutevals(rn, scmap, species(rn), species(rn))
+    end
+
+    if !all(r -> ismassaction(r, rn), rxs)
+        error("The supplied ReactionSystem has reactions that are not ismassaction. The mass action vector is only defined for pure mass action networks.")
+    end
+
+    vtype = eltype(specs) <: Symbolics.BasicSymbolic ? Num : eltype(specs)
+    Φ = Vector{vtype}()
+    rcmap = reactioncomplexmap(rn)
+    for comp in keys(reactioncomplexmap(rn))
+        subs = map(ce -> getfield(ce, :speciesid), comp)
+        stoich = map(ce -> getfield(ce, :speciesstoich), comp)
+        maprod = prod(vtype[specs[s]^α for (s, α) in zip(subs, stoich)])
+        combinatoric_ratelaws && (maprod /= prod(map(factorial, stoich)))
+        push!(Φ, maprod)
+    end
+
+    Φ
+end
+
+function massactionvector(rn::ReactionSystem, scmap::Tuple; combinatoric_ratelaws = Catalyst.get_combinatoric_ratelaws(rn))
+    sdict = Dict(scmap)
+    massactionvector(rn, sdict; combinatoric_ratelaws)
+end
+
+function massactionvector(rn::ReactionSystem, scmap::Vector; combinatoric_ratelaws = Catalyst.get_combinatoric_ratelaws(rn)) 
+    sdict = Dict(scmap)
+    massactionvector(rn, sdict; combinatoric_ratelaws)
+end
+
 @doc raw"""
     complexoutgoingmat(network::ReactionSystem; sparse=false)
 
@@ -787,7 +924,7 @@ function isdetailedbalanced(rs::ReactionSystem, parametermap::Dict; abstol=0, re
     elseif !isreversible(rs)
         return false
     elseif !all(r -> ismassaction(r, rs), reactions(rs))
-        error("The supplied ReactionSystem has reactions that are not ismassaction. Testing for being complex balanced is currently only supported for pure mass action networks.")
+        error("The supplied ReactionSystem has reactions that are not ismassaction. Testing for being detailed balanced is currently only supported for pure mass action networks.")
     end
 
     isforestlike(rs) && deficiency(rs) == 0 && return true