随着细胞等介观体系中研究的深入,介观生化反应的模拟方法曰益引起人们的关注.一方面,这类体系中涨落效应显著,化学反应本质上是随机的离散过程,经典的确定性模拟方法不再适用,需要使用随机模拟方法；另一方面,这种体系中子反应的速率和反应物的分子数目可能有显著差别,存在多尺度特性,使一般随机算法的模拟效率极低,对反应的准确高效模拟提出了新的要求.本文简述了Gillespie提出的一系列基本随机算法,综述了近年来发展的针对反应多尺度特性的改进随机算法和混合算法,介绍了各类算法的特点、实现的技术问题及相对其他算法和实际生物信号描述表现出的优缺点.%With the development of investigations in mesoscopic systems such as the cellular systems,growing attention has been focused on the corresponding reaction dynamics simulation methods.For one thing,fluctuations are significant in these systems,chemical reactions are essentially discrete stochastic processes,classical deterministic algorithms are not feasible,stochastic simulation methods are required.For another,there are multiscale characters in these systems.The coexistence of fast and slow sub-reactions produce multi-time scales and the different molecular abundance of various reactants exhibit multi-population scales,These multiscale characters will considerably reduce the simulation efficiency of stochastic algorithms.Therefore,there are even higher requirements for accurate but efficient reaction dynamics simulation algorithms.In this paper,we first summarize the basic stochastic simulation algorithms developed by Gillespie,then review the recently proposed improved stochastic algorithms and hybrid methods which are developed to circumvent the multiscaled problems.The special characters,the technical problems involved in the implementation and the advantages and disadvantages of these algorithms are introduced.
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