文献类型：期刊文献

英文题名：On structural identifiability analysis of the cascaded linear dynamic systems in isotopically non-stationary 13C labelling experiments

作者：Lin, Weilu[1];Wang, Zejian[1];Huang, Mingzhi[1];Zhuang, Yingping[1];Zhang, Siliang[1]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2018

卷号：300

起止页码：122

外文期刊名：MATHEMATICAL BIOSCIENCES

收录：;EI(收录号：20181504992907);WOS:【SCI-EXPANDED(收录号:WOS:000437078500012)】；

基金：Authors thank financial support by Open Funding Project of the State Key Laboratory of Bioreactor Engineering (F200-B-01G), National Science Foundation of China (31200024/C010201), National Key Technology Support Program of China (No.2011BAF02B05), National Key Basic Research Program of China (973 Program, 2013CB733605). Authors also thank valuable suggestions from anonymous reviewers to improve the quality of the manuscript.

语种：英文

外文关键词：Isotopically non-stationary 13C labelling experiments; Structural identifiability; Taylor series expansion; Non-stationary cumomer balance equations

摘要：The isotopically non-stationary 13C labelling experiments, as an emerging experimental technique, can estimate the intracellular fluxes of the cell culture under an isotopic transient period. However, to the best of our knowledge, the issue of the structural identifiability analysis of non-stationary isotope experiments is not well addressed in the literature. In this work, the local structural identifiability analysis for non-stationary cumomer balance equations is conducted based on the Taylor series approach. The numerical rank of the Jacobian ma-trices of the finite extended time derivatives of the measured fractions with respect to the free parameters is taken as the criterion. It turns out that only one single time point is necessary to achieve the structural identifiability analysis of the cascaded linear dynamic system of non-stationary isotope experiments. The equivalence between the local structural identifiability of the cascaded linear dynamic systems and the local optimum condition of the nonlinear least squares problem is elucidated in the work. Optimal measurements sets can then be determined for the metabolic network. Two simulated metabolic networks are adopted to demonstrate the utility of the proposed method.