Sensitivity Analysis (SA) provides techniques which can be used to identify the parameters which have the greatest influence on the results obtained from a model. Classical SA methods apply to deterministic simulations of ODE models. We extend these to stochastic simulations and consider the analysis of models with bifurcation points and bistable behaviour. We consider local, global and screening SA methods applied to multiple runs of Gillespie's Stochastic Simulation Algorithm (SSA). We present an example of stochastic sensitivity analysis of a real pathway, the MAPK signalling pathway.
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