A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

被引:1015
|
作者
Feist, Adam M.
Henry, Christopher S.
Reed, Jennifer L.
Krummenacker, Markus
Joyce, Andrew R.
Karp, Peter D.
Broadbelt, Linda J.
Hatzimanikatis, Vassily
Palsson, Bernhard O.
机构
[1] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA
[2] Northwestern Univ, McCormick Sch Engn & Appl Sci, Dept Chem & Biol Engn, Evanston, IL 60201 USA
[3] SRI Int, Bioinformat Res Grp, Ravenswood, CA USA
[4] Ecole Polytech Fed Lausanne, Lab Computat Syst Biotechnol, CH-1015 Lausanne, Switzerland
关键词
computational biology; group contribution method; systems biology; thermodynamics; ADAPTIVE EVOLUTION; BIOMASS COMPOSITION; HIGH-THROUGHPUT; MODELS; ENERGY; CAPABILITIES; NETWORK; CONSTRAINTS; ANNOTATION; EFFICIENCY;
D O I
10.1038/msb4100155
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
引用
收藏
页数:18
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