SEPARATION OF TIME-SCALES AND MODEL REDUCTION FOR STOCHASTIC REACTION NETWORKS

• 成果类型: Article
• 署名作者: Kang, Hye-Won; Kurtz, Thomas G.
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/12-AAP841
• 发表日期: 2013
• 页码: 529-583
• 关键词: steady-state assumption; differential-equations; CONVERGENCE; simulation; kinetics

摘要

A stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is developed for determining appropriate choices of the exponents that can be applied to large complex networks. When the scaling implies subnetworks have different time-scales, the subnetworks can be approximated separately, providing insight into the behavior of the full network through the analysis of these lower-dimensional approximations.