Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions.

TitleGap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions.
Publication TypeJournal Article
Year of Publication2012
AuthorsOrth JD, Palsson BO
JournalBMC Syst Biol
Volume6
Issue1
Pagination30
PubMed Date2012-5-3
ISSN1752-0509
Abstract

ABSTRACT: BACKGROUND: The iJO1366 reconstruction of the metabolic network of Escherichia coli K-12 MG1655 is one of the most complete and accurate metabolic reconstructions available for any organism. Since our knowledge of this well-studied model organism is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the reconstructed network. RESULTS: A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions. CONCLUSIONS: Numerous improvements to the iJO1366 metabolic reconstruction were suggested by implementing the new workflow. Experiments were performed to verify a subset of the computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.

PubMed ID22548736

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