Maximum entropy decomposition of flux distribution at steady state to elementary modes

被引：21

作者：

Zhao, Quanyu ^{[1
]}

Kurata, Hiroyuki ^{[1
]}

机构：

[1] Kyushu Inst Technol, Dept Biosci & Bioinformat, Fukuoka 8208502, Japan

来源：

JOURNAL OF BIOSCIENCE AND BIOENGINEERING | 2009年 / 107卷 / 01期

关键词：

Maximum entropy principle; Metabolic flux analysis; Enzyme control flux; Elementary mode; Linear programming; Quadratic programming; METABOLIC NETWORKS REVEALS; ESCHERICHIA-COLI; SACCHAROMYCES-CEREVISIAE; GENE-EXPRESSION; PATTERNS; INTEGRATION; RESIDUES; MUTANT; SPACE;

D O I：

10.1016/j.jbiosc.2008.09.011

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Enzyme Control Flux (ECF) is a method of correlating enzyme activity and flux distribution. The advantage of ECF is that the measurement integrates proteome data with metabolic flux analysis through Elementary Modes (EMs). But there are a few methods of effectively determining the Elementary Mode Coefficient (EMC) in cases where no objective biological function is available. Therefore, we proposed a new algorithm implementing the maximum entropy principle (MEP) as an objective function for estimating the EMC. To demonstrate the feasibility of using the MEP in this way, we compared it with Linear Programming and Quadratic Programming for modeling the metabolic networks of Chinese Hamster Ovary, Escherichia coli, and Saccharomyces cerevisiae cells. The use of the MEP presents the most plausible distribution of EMCs in the absence of any biological hypotheses describing the physiological state of cells, thereby enhancing the prediction accuracy of the flux distribution in various mutants. (C) 2008, The Society for Biotechnology, Japan. All rights reserved.

引用

页码：84 / 89

页数：6

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