文献类型：期刊文献

英文题名：Effective sampling trajectory optimisation for sensitivity analysis of biological systems

作者：Xu, Zhao Z.[1];Liu, Ji[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimisat Chem Proc, Coll Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：13

期号：3

起止页码：109

外文期刊名：IET SYSTEMS BIOLOGY

收录：;EI(收录号：20192407035616);WOS:【SCI-EXPANDED(收录号:WOS:000502760300002)】；

基金：The authors would like to appreciate the editors and reviewers for their valuable comments and kind help. This research was supported by the Foundation of Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai, 200240 and Chinese National Natural Science Foundation (no. 61573144 and no. 61673175).

语种：英文

外文关键词：Trajectories - Ordinary differential equations - Aerodynamics - Biological systems

摘要：Sensitivity analysis has been widely applied to study the biological systems, including metabolic networks, signalling pathways, and genetic circuits. The Morris method is a kind of screening sensitivity analysis approach, which can fast identify a few key factors from numerous biological parameters and inputs. The parameter or input space is randomly sampled to produce a very limited number of trajectories for the calculation of elementary effects. It is clear that the sampled trajectories are not enough to cover the whole uncertain space, which eventually causes unstable sensitivity measures. This paper presents a novel trajectory optimisation algorithm for the Morris-based sensitivity calculation to ensure a good scan throughout the whole uncertain space. The paper demonstrates that this presented method gets more consistent sensitivity results through a benchmark example. The application to a previously published ordinary differential equation model of a cellular signalling network is presented. In detail, the parameter sensitivity analysis verifies the good agreement with data of the literatures.