Binomial distribution based τ-leap accelerated stochastic simulation -: art. no. 024112
Binomial distribution based τ-leap accelerated stochastic simulation -: art. no. 024112
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DOI：
10.1063/1.1833357
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发表时间：
2005-01-08
影响因子：
4.4
通讯作者：
Katsoulakis, MA
中科院分区：
文献类型：
--
作者：
Chatterjee, A;Vlachos, DG;Katsoulakis, MA
最近，Gillesbie提出了求解均匀混合反应系统的加速随机蒙特卡罗方法[J。太棒了。115、1716(2001)]。在该方法每个时间增量中，执行从泊松分布中随机选择的多个反应事件，以实现较长时间的模拟。这里我们介绍一种二项分布的tau-leap算法(简称BD-tau方法)。这种方法将二项分布变量的有界性质与极限反应物和约束激发概念相结合，避免了在大时间增量的原始吉列斯皮方法中遇到的负总体，从而节省了质量。使用原型反应网络的模拟结果表明，BD-tau方法在时间上比原始方法在可比粗粒化方面更准确。(C)2005年美国物理研究所。
Recently, Gillespie introduced the tau-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution tau-leap algorithm (abbreviated as BD-tau method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original tau-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-tau method is more accurate than the original method for comparable coarse-graining in time. (C) 2005 American Institute of Physics.