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

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