Parallel replica dynamics method for bistable stochastic reaction networks: simulation and sensitivity analysis
For researchers studying bistable stochastic systems, this work provides an easy-to-implement parallel method to overcome rare transition sampling bottlenecks.
The paper applies the parallel replica (ParRep) method to accelerate stationary distribution sampling for bistable stochastic reaction networks, demonstrating efficiency and accuracy on the Schlögl model and genetic switches network.
Stochastic reaction networks that exhibit bistability are common in many fields such as systems biology and materials science. Sampling of the stationary distribution is crucial for understanding and characterizing the long term dynamics of bistable stochastic dynamical systems. However, this is normally hindered by the insufficient sampling of the rare transitions between the two metastable regions. In this paper, we apply the parallel replica (ParRep) method for continuous time Markov chain to accelerate the stationary distribution sampling of bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions and it is very easy to implement. We combine ParRep with the path space information bounds for parametric sensitivity analysis. We demonstrate the efficiency and accuracy of the method by studying the Schlögl model and the genetic switches network.