Towards Kinetic Modeling of Global Metabolic Networks： Methylobacterium extorquens AM1 Growth as Validation

作     者：Ping Ao Lik Wee Lee Mary E. Lidstrom Lan Yin Xiaomei Zhu 

作者机构：Department of Mechanical Engineering University of Washington Seattle WA 98195 USA Department of Physics University of Washington Seattle WA 98195 USA Department of Microbiology University of Washington Seattle WA 98195 USA Department of Chemical Engineering University of Washington Seattle WA 98195 USA School of Physics Peking University 100871 Beijing PR China GeneMath 5525 27^th Ave. N.E. Seattle WA 98105 USA 

出 版 物：《生物工程学报》 (Chinese Journal of Biotechnology)

年 卷 期：2008年第24卷第6期

页      面：980-994页

核心收录：

学科分类：0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 09[农学] 0836[工学-生物工程] 

基　　金：USA National Institutes of Health(Nos.K25-HG002894-05(P.A.) GM36296(L.W.L.and M.E.L.) 

主　　题：生物科学 新陈代谢 动力学 丝氨酸 

摘      要：Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, gluconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: 1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; 2) using a set of steady state fluxes and metabolite concentrations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; 3) choosing enzyme constants K s and K eqs optimized for reactions under physiological concentrations, if their experimental values are unknown; 4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.