改进的τ-leap算法在生化反应系统随机模拟中的应用  

Application of Improvedτ-leap Algorithm in Stochastic Simulation of Biochemical Reaction Systems

作　　者：刘焕[1] 彭新俊[2,3] 周文[4] 王翼飞[1] 

机构地区：[1]上海大学数学系,上海200444 [2]上海师范大学数学系,上海200234 [3]上海市高校科学计算重点实验室,上海200234 [4]安徽师范大学数学与计算机学院,安徽芜湖241000

出　　处：《应用科学学报》2009年第3期266-271,共6页Journal of Applied Sciences

基　　金：国家自然科学基金(N0.30871341);国家"863"高技术研究发展计划基金(No.2006AA02Z190)项目

摘　　要：提出了一种τ-选择策略,有效地反映了生化反应系统中分子数目的改变.并由此提出了改进的τ-leap(improvedτ-leap)算法,该算法对生化反应系统的随机模拟更为有效和实用.并以两个生化反应系统模型为例,分别用精确的SSA算法、改进的τ-leaping算法以及已有的修正的τ-leap(modified tau-leap)算法进行了模拟计算.仿真实验结果表明:在具有同等计算复杂度的情况下,改进的τ-leap算法较修正的τ-leap明显地提高了模拟精度.A τ-selected strategy that ascertain the r value is proposed in this paper. It is developed as an improved τ-leap algorithm. The τ-selected strategy can effectively reflect change numbers of the species in biochemical systems, while the improved τ-leaping algorithm is more effective and practical to stochastic simulation of biochemical reaction systems than other algorithms. We take two models of biochemical reaction systems as examples, and carry out simulation using accurate SSA algorithm, improved τ-leaping algorithm, and the present modified τ-leap respectively. Numerical results demonstrate that, compared with modified τ-leap algorithm, the improved τ-leap algorithm can significantly increase simulation precision with the same computation complexity under the same condition.

关 键 词：随机模拟算法 τ-选择策略 τ-leap算法 生化反应系统 

分 类 号：O644[理学—物理化学]