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Stochastic reaction networks that exhibit bistability are common in manyfields such as systems biology and materials science. Sampling of thestationary distribution is crucial for understanding and characterizing thelong term dynamics of bistable stochastic dynamical systems. However, this isnormally hindered by the insufficient sampling of the rare transitions betweenthe two metastable regions. In this paper, we apply the parallel replica(ParRep) method for continuous time Markov chain to accelerate the stationarydistribution sampling of bistable stochastic reaction networks. The proposedmethod uses parallel computing to accelerate the sampling of rare transitionsand it is very easy to implement. We combine ParRep with the path spaceinformation bounds for parametric sensitivity analysis. We demonstrate theefficiency and accuracy of the method by studying the Schl\"{o}gl model and thegenetic switches network.

Parallel replica dynamics method for bistable stochastic reaction networks: simulation and sensitivity analysis