摘要
该文重新定义了临界反应,以更合理地界定可能出现负分子组分的反应.在此基础上提出了加速τ-leap算法,使系统中某些反应物数目较少时也可以采用τ-leap方法进行模拟,同时提高了模拟速度.以两个生化反应系统模型为例,分别用精确的随机模拟算法、修正τ-leap算法和加速τ-leap算法进行模拟运算,结果表明加速τ-leap算法在保证精度的同时能有效提高模拟速度.
This paper redefines critical reaction. It is more reasonable to demarcate reactions which may have negative reactants. Based on the definition, the accelerated T-leap algorithm is proposed. Even if the number of some species in biochemical systems is small, the T-leap algorithm is adaptive. This algorithm also increases the speed of simulation. The accurate stochastic simulation algorithm (SSA) algorithm, an improved T-leap algorithm and the present accelerated algorithm are compared in two biochemical reaction models. Numerical results demonstrate that the proposed method is faster under the same simulation precision.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2011年第2期203-208,共6页
Journal of Applied Sciences
基金
国家自然科学基金(No.30971480)
国家科技重大专项基金(No.2009ZX09103-686)
上海市教委重点学科建设项目基金(No.J50101)
上海市重点学科建设项目基金(No.S30104)资助
关键词
步长选择策略
临界反应
τ-leap算法
随机模拟算法
生化反应系统
step size selection strategy, critical reaction, T-leap method, stochastic simulation algorithm,biochemical reaction system