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Systems approach to refining genome annotation.

ABSTRACT: Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

SUBMITTER: Reed JL

PROVIDER: S-EPMC1859954 | biostudies-literature | 2006 Nov

REPOSITORIES: biostudies-literature

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Systems approach to refining genome annotation.

Reed Jennifer L JL Patel Trina R TR Chen Keri H KH Joyce Andrew R AR Applebee Margaret K MK Herring Christopher D CD Bui Olivia T OT Knight Eric M EM Fong Stephen S SS Palsson Bernhard O BO

Proceedings of the National Academy of Sciences of the United States of America 20061106 46

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, ...[more]

PMID: 17088549

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