# gillespie: Simulate a sample path from a stochastic kinetic model... In smfsb: Stochastic Modelling for Systems Biology

## Description

This function simulates a single realisation from a discrete stochastic kinetic model described by a stochastic Petri net (SPN).

## Usage

 `1` ```gillespie(N, n, ...) ```

## Arguments

 `N` An R list with named components representing a stochastic Petri net (SPN). Should contain `N\$M`, a vector representing the initial marking of the net, `N\$Pre`, a matrix representing the LHS stoichiometries, `N\$Post`, a matrix representing the RHS stoichiometries, and `N\$h`, a function representing the rates of the reaction processes. `N\$h` should have first argument `x`, a vector representing the current state of the system, and second argument `t`, a scalar representing the current simulation time (in the typical time-homogeneous case, `N\$h` will ignore this argument). `N\$h` may posses additional arguments, representing reaction rates, for example. `n` An integer representing the number of events to simulate, excluding the initial state, `N\$M`. `...` Additional arguments (such as reactions rates) will be passed into the function `N\$h`.

## Value

A list with first component `t`, a vector of length `n` containing event times and second component `x`, a matrix with `n+1` rows containing the state of the system. The `i`th row of `x` contains the state of the system prior to the `i`th event.

`simpleEuler`, `rdiff`, `discretise`, `gillespied`, `StepGillespie`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# load the LV model data(spnModels) # simulate a realisation of the process and plot it out = gillespie(LV,10000) op = par(mfrow=c(2,2)) plot(stepfun(out\$t,out\$x[,1]),pch="") plot(stepfun(out\$t,out\$x[,2]),pch="") plot(out\$x,type="l") # use the "discretise" function to map it to an R "ts" object plot(discretise(out,dt=0.01),plot.type="single",lty=c(1,2)) par(op) ```

### Example output ```
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smfsb documentation built on May 2, 2019, 5:13 a.m.