Parallel replica dynamics method for bistable stochastic reaction networks: simulation and sensitivity analysis
arXiv:1705.06807 · doi:10.1063/1.5017955
Abstract
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.
7 figures
References in corpus (6)
- Noise-induced multistability in chemical systems: Discrete vs Continuum modeling
- Exponential sensitivity of noise-driven switching in genetic networks
- Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability
- Accurate Chemical Master Equation Solution Using Multi-Finite Buffers
- A Relative Entropy Rate Method for Path Space Sensitivity Analysis of Stationary Complex Stochastic Dynamics
- State space truncation with quantified errors for accurate solutions to discrete Chemical Master Equation