A Distributed-Based Stochastic Simulation Algorithm for Large Biochemical Reaction Networks

Dynamic modeling and simulation of biochemical reaction networks is an important topic in systems biology and is obtaining growing attention from researchers with computational and biological background. Gillespie's stochastic simulation algorithm (SSA) was a standard algorithm to simulate "well- stirred" biochemical reaction system. The biggest problem of the SSA lied in the excessive cost of computation because of its computational complexity. This paper describes a new distributed-based stochastic simulation algorithm (DSSA), which uses distributed computing and multi-agents system to improve the computing performance of the SSA. Experiments showed the DSSA is able to improve time performance significantly when compared to the SSA, and is an effective way to model and simulate large biochemical reaction networks.