pwStochasticSimulation

 

 [Ts, Ns] = pwStochasticSimulation(x0,n0,k,c,D,L,tf)

 Stochastic Gillespie simulation for the highest selected model.

 This function is a PottersWheel wrapper for the published Matlab
 script of Ullah et al. available at
 http://www.sbi.uni-rostock.de/publications/matlab-paper/stoch.m

 It can be applied in conjunction with

  pwGetStoichiometricMatrix

Arguments for pwStochasticSimulation

 x0 Column vector of initial concentrations of all involved species
 n0 Column Vector of initial populations of all the species involved
 k  Row vector of rate constants of all elementary reactions
 c  Row vector of stochastic rate constants of all elementary reactions
 D  Stoichiometry matrix with rows correspoding to species and columns
     corresponding to reaction channels
 L  Stoichiometry matrix for reactants only such that L = -D.*(D < 0);
 tf Final time of simulation

 Ts Row vector of time points of reaction events
 Ns Matrix of output concentrations with a column for each time point.

Description

 
  [Ts,Ns] = pwStochasticSimulation(x0,n0,k,c,D,L,tf)

 performs a stochastic simulation of a biochemical network composed of
 M elementary reactions, using the Gillespie algorithm.

  [Ts,Ns] = pwStochasticSimulation(x0,n0,k,c,D,L,tf,R)

 performs R runs of stochastic simulation. The outputs Ts and Ns are cell
 arrays where each cell corresponds to one run.

  [Ts,Ns,TT,NBAR] = pwStochasticSimulation(x0,n0,k,c,D,L,tf,R)

 where R>1, also returns the ensemble of times in the row vector TT and
 the mean of Ns over R runs in matrix NBAR of the size of Ns.

  pwStochasticSimulation(x0,n0,k,c,D,L,tf,1)

 is the same as

  pwStochasticSimulation(x0,n0,k,c,D,L,tf).

Reference

 
 Ullah et al, IEE Proc.-Syst. Biol., Vol. 153, No. 2, March 2006
 Deterministic modelling and stochastic simulation of
 biochemical pathways using MATLAB

See also

pwGetStoichiometricMatrix