DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

作     者：Yang, Laurence Ebrahim, Ali Lloyd, Colton J. Saunders, Michael A. Palsson, Bernhard O. 

作者机构：Univ Calif San Diego Dept Bioengn 9500 Gilman Dr La Jolla CA 92093 USA Stanford Univ Dept Management Sci & Engn 475 Via Ortega Stanford CA 94305 USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark 

出 版 物：《BMC SYSTEMS BIOLOGY》 (BMC系统生物学)

年 卷 期：2019年第13卷第1期

页      面：2-2页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

基　　金：National Institute of General Medical Sciences of the National Institutes of Health [U01GM102098, R01GM057089] Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark [NNF10CC1016517] 

主　　题：Constraint-based modeling Metabolism Proteome Dynamic simulation Batch culture 

摘      要：BackgroundGenome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic *** develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (inertia) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative ***, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.