CME models

Stochastic reaction network model.

Fields

stoich_matrix::Matrix{IntT}

(Net) stoichiometry matrix. Column i represents the net change to molecular counts due to reaction i.

propensities::Vector{PT}

Propensity functions.

parameters::AbstractVector

Parameters.

Examples

The following code constructs a CmeModel instance for the two-state telegraphic gene expression model.

𝕊 = [[-1, 1, 0] [1, -1, 0] [0, 0, 1] [0, 0, -1]] # Net stoichiometry matrix for 3 species `inactive_gene`, `activated_gene`, `mRNA`
a1 = propensity() do x, p
    p[1] * x[1]
end
a2 = propensity() do x, p
    p[2] * x[2]
end
a3 = propensity() do x, p
    p[3] * x[2]
end
a4 = propensity() do x, p
    p[4] * x[3]
end
k₀₁ = 0.05
k₁₀ = 0.1
λ = 5.0
γ = 0.5
θ = [k₀₁, k₁₀, λ, γ]
model = CmeModel(𝕊, [a1,a2,a3,a4], θ)

See also

CmeModelWithSensitivity, Propensity, StandardTimeInvariantPropensity, JointTimeVaryingPropensity, SeparableTimeVaryingPropensity.

CmeModelWithSensitivity

Stochastic reaction network model with information about gradient of the propensity functions with respect to parameters.

See also

PropensityGradient

CmeModelWithSensitivity(stoich_matrix, propensities::Vector{PT}, parameters::AbstractVector) where {PT <: Propensity}

Construct a CME model with sensitivity information using a net stoichiometry matrix stoich_matrix and propensity functions propensities and parameters parameters. The partial derivatives of the propensity functions with respect to parameters are automatically derived using automatic differentiation.

CmeModelWithSensitivity(model::CmeModel)

Construct a CME model with sensitivity from a CME model (without sensitivity information) using ForwardDiff.jl automatic differentiation.

get_gradient_sparsity_patterns(
    model::CmeModelWithSensitivity
) -> SparseArrays.SparseMatrixCSC

Get the sparse matrix representing dependency pattern of propensity functions of model parameters.

get_parameter_count(model::CmeModelWithSensitivity) -> Any

Get number of parrameters.

get_parameter_count(model::CmeModel) -> Any

Get number of parameters.

get_parameters(
    model::CmeModelWithSensitivity
) -> AbstractVector{T} where T

Get parameter vectors of a Chemical Master Equation model instance.

get_parameters(model::CmeModel) -> AbstractVector{T} where T

Get the vector of model parameters.

get_propensities(
    model::CmeModelWithSensitivity
) -> Vector{var"#s17"} where var"#s17"<:Propensity

Get the vector of propensities.

get_propensities(
    model::CmeModel
) -> Vector{var"#s17"} where var"#s17"<:Propensity

Get vector of propensities.

get_propensity_gradients(
    model::CmeModelWithSensitivity
) -> Vector{var"#s17"} where var"#s17"<:PropensityGradient

Get the propensity gradients.

get_reaction_count(model::CmeModelWithSensitivity) -> Int64

Get number of reactions in the model.

get_reaction_count(model::CmeModel) -> Int64

Get number of reactions.

get_species_count(model::CmeModelWithSensitivity) -> Int64

Get the number of species (i.e., the length of the CME state vector).

get_species_count(model::CmeModel) -> Int64

Get number of species.

get_stoich_matrix(
    model::CmeModelWithSensitivity
) -> Matrix{IntT} where IntT<:Integer

Get the net stoichiometry matrix.

get_stoich_matrix(
    model::CmeModel
) -> Matrix{IntT} where IntT<:Integer

Get net stoichiometry matrix.