Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches

JOURNAL OF COMPUTATIONAL PHYSICS

卷:375

Article

Lester, Christopher¹  Yates, Christian A.²  Baker, Ruth E.¹ 

[1] Math Inst, Woodstock Rd, Oxford OX2 6GG, England

[2] Dept Math Sci, Bath BA2 7AY, Avon, England

关键词: Biochemical reaction networks;    Stochastic simulation;    Multi-level Monte Carlo approaches;    Variance reduction;   

DOI  :  10.1016/j.jcp.2018.06.045

来源: Elsevier

【 摘 要 】

In this work, we consider the problem of estimating summary statistics to characterise biochemical reaction networks of interest. Such networks are often described using the framework of the Chemical Master Equation (CME). For physically-realistic models, the CME is widely considered to be analytically intractable. A variety of Monte Carlo algorithms have therefore been developed to explore the dynamics of such networks empirically. Amongst them is the multi-level method, which uses estimates from multiple ensembles of sample paths of different accuracies to estimate a summary statistic of interest. In this work, we develop the multi-level method in two directions: (1) to increase the robustness, reliability and performance of the multi-level method, we implement an improved variance reduction method for generating the sample paths of each ensemble; and (2) to improve computational performance, we demonstrate the successful use of a different mechanism for choosing which ensembles should be included in the multi-level algorithm. (C) 2018 Elsevier Inc. All rights reserved.

【 授权许可】

Free