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

TL;DR

This paper demonstrates that the weighted ensemble simulation strategy can efficiently and accurately simulate complex chemical kinetics networks, significantly outperforming traditional methods in sampling rare events and calculating mean first passage times.

Contribution

It adapts the weighted ensemble method to systems biology models, enabling efficient simulation of rare events without extensive parameter tuning.

Findings

Achieved speedups of 10^12 to 10^20 for rare state sampling.

Produced accurate joint probability distributions over time.

Enhanced precision in characterizing slow processes.

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.