Interprets the provided model as a stochastic model

The chemical master equation can be simulated as a Markov jump process (or continuous time Markov chain). One of the stochastic solver algorithms is the Gillespie algorithm. This function return sa data structure that can be used to generate code for the Gillespie solver in this package.

Usage

as_cme(m)

Arguments

m

list of data.frames, obtained via model_from_tsv()

Value

a list containing the interpreted model.

Details

This function interprets the contionuous model m as a discrete state model with molecule counts and propensities. For this reason, we need to specify a volume for the simulations to take place in.

The model m is assumed to describe a reaction network, as a list of data.frames (as retuned by model_from_tsv). The systems biology information in the file is assumed to be concentrations and rate coefficients, regardless of the interpretation this function will derive from it. This is to make the model format of the TSV file fairly uniform and independent of how we want to solve the derived equations, be it ODE or CME.

Like the ode object, the returned object can also store the paths of files we create for this model, with: c_path<-, and so_path<-

With the information provided with the rate coefficient units and a volume, this function tries to convert everything to Gillespie rate constants.

Examples

m <- model_from_tsv(uqsa_example("AKAR4"))
stochasticModel <- as_cme(m)
print(stochasticModel)
#>                       Name : AKAR4
#> 
#> 
#>  Number of state variables : 4
#>       Number of parameters : 3
#>          Number of outputs : 1
#>        Number of constants : 0