An efficient finite-difference strategy for sensitivity analysis of stochastic models of biochemical systems

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• 作者：Monjur Morshed^a ; ^{monjur.morshed@uwaterloo.ca} ; Brian Ingalls^a ; ^{bingalls@uwaterloo.ca} ; Silvana Ilie^b ; ^{silvana@ryerson.ca}
• 关键词：Stochastic simulation algorithm ; Stochastic models of biochemical kinetics ; tau-Leaping method ; Sensitivity analysis ; Adaptive time-stepping ; Chemical Master Equation
• 刊名：Biosystems
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：151
• 期：Complete
• 页码：43-52
• 全文大小：2037 K
• 卷排序：151

文摘

Sensitivity analysis characterizes the dependence of a model's behaviour on system parameters. It is a critical tool in the formulation, characterization, and verification of models of biochemical reaction networks, for which confident estimates of parameter values are often lacking. In this paper, we propose a novel method for sensitivity analysis of discrete stochastic models of biochemical reaction systems whose dynamics occur over a range of timescales. This method combines finite-difference approximations and adaptive tau-leaping strategies to efficiently estimate parametric sensitivities for stiff stochastic biochemical kinetics models, with negligible loss in accuracy compared with previously published approaches. We analyze several models of interest to illustrate the advantages of our method.