Efficient Stochastic Simulation of Chemical Kinetics Networks using a Weighted Ensemble of Trajectories

Abstract

We apply the "weighted ensemble" (WE) simulation strategy, previously employed in the context of molecular dynamics simulations, to a series of systems-biology models that range in complexity from one-dimensional to a system with 354 species and 3680 reactions. WE is relatively easy to implement, does not require extensive hand-tuning of parameters, does not depend on the details of the simulation algorithm, and can facilitate the simulation of extremely rare events. For the coupled stochastic reaction systems we study, WE is able to produce accurate and efficient approximations of the joint probability distribution for all chemical species for all time t. WE is also able to efficiently extract mean first passage times for the systems, via the construction of a steady-state condition with feedback. In all cases studied here, WE results agree with independent calculations, but significantly enhance the precision with which rare or slow processes can be characterized. Speedups over "brute-force" in sampling rare events via the Gillespie direct Stochastic Simulation Algorithm range from ~10^12 to ~10^20 for rare states in a distribution, and ~10^2 to ~10^4 for finding mean first passage times.

11 pages, 6 figures, 4 supplemental files

References in corpus (5)

• Forward Flux Sampling-type schemes for simulating rare events: Efficiency analysis
• Simulating rare events using a Weighted Ensemble-based string method
• Efficient Stochastic Simulation of Chemical Kinetics Networks using a Weighted Ensemble of Trajectories
• Non-Stationary Forward Flux Sampling
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