AESS: Accelerated Exact Stochastic Simulation

Abstract The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulatio... Title of program: AESS Catalogue Id: AEJW_v1_0 Nature of problem Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Versions of this program held in the CPC repository in Mendeley Data AEJW_v1_0; AESS; 10.1016/j.cpc.2011.07.013 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

摘要 吉莱斯皮（Gillespie）提出的随机模拟算法（Stochastic Simulation Algorithm，SSA）为探究物种种群规模较小或存在显著噪声影响的化学系统行为提供了强有力的研究手段。系统生物学领域的基因回路模拟与生态学应用通常都会采用该随机模拟算法（SSA）。该算法的计算成本往往较高，因此研究者们始终致力于寻求高效的SSA实现方案。本程序包即围绕加速精确随机模拟（Accelerated Exact Stochastic Simulation）开发。
程序名称：AESS；目录编号：AEJW_v1_0。
问题特性：针对物种种群规模较低的化学系统，可通过吉莱斯皮的随机模拟方法实现精准仿真。目前已衍生出诸多原始随机模拟算法的变体，其中部分方法所得统计结果可严格匹配化学主方程（Chemical Master Equation，CME），另有部分方法则对化学主方程进行近似处理。
存放在Mendeley数据平台的计算机物理学报（Computer Physics Communications，CPC）库中的该程序版本为AEJW_v1_0；关联标识：AESS；DOI:10.1016/j.cpc.2011.07.013。本程序源自贝尔法斯特女王大学维护的CPC程序库（1969-2018年）。