期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 16, 期 9, 页码 1195-1208出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2008.0249
关键词
algorithms; evolution; statistics; stochastic processes
类别
资金
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM008185, R01GM053275] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R37MH059490, R01MH059490] Funding Source: NIH RePORTER
- NIGMS NIH HHS [GM53275, GM008185] Funding Source: Medline
- NIMH NIH HHS [MH59490] Funding Source: Medline
Stochastic simulation methods are important in modeling chemical reactions, and biological and physical stochastic processes describable as continuous-time discrete-state Markov chains with a finite number of reactant species and reactions. The current algorithm of choice, tau-leaping, achieves fast and accurate stochastic simulation by taking large time steps that leap over individual reactions. During a leap interval (t, t + tau) in tau-leaping, each reaction channel operates as a Poisson process with a constant intensity. We modify tau-leaping to allow linear and quadratic changes in reaction intensities. Because our version of tau-leaping accurately anticipates how intensities change over time, we propose calling it the step anticipation tau-leaping (SAL) algorithm. We apply SAL to four examples: Kendall's process, a two-type branching process, Ehrenfest's model of diffusion, and Michaelis-Menten enzyme kinetics. In each case, SAL is more accurate than ordinary tau-leaping. The degree of improvement varies with the situation. Near stochastic equilibrium, reaction intensities are roughly constant, and SAL and ordinary tau-leaping perform about equally well.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据