The Escherichia coli MG1655 in Silico Metabolic Genotype: Its Definition, Characteristics, and Capabilities

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 97; no. 10; pp. 5528 - 5533
• Main Authors: Edwards, J. S., Palsson, B. O.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences of the United States of America 09-05-2000; National Acad Sciences; National Academy of Sciences; The National Academy of Sciences
• Subjects: Bacteria; Bacterial Proteins - genetics; Bacterial Proteins - metabolism; Biochemistry; Biological Sciences; Biomass; Cellular metabolism; Chromosome Mapping; Enzymes - genetics; Enzymes - metabolism; Escherichia coli; Escherichia coli - genetics; Escherichia coli - metabolism; Gene deletion; Genome, Bacterial; Genomes; Genomics; Genotype; Genotypes; Kinetics; Mass balance; Metabolism; Microbiology; Models, Biological; Models, Chemical; Molecular biology; Salmonella
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.97.10.5528

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.