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Systematizing the generation of missing metabolic knowledge

Authors

  • Jeffrey D. Orth,

    1. Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093-0412, USA; telephone: 1-858-534-5668; fax: 1-858-822-3120
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  • Bernhard Ø. Palsson

    1. Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093-0412, USA; telephone: 1-858-534-5668; fax: 1-858-822-3120
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Abstract

Genome-scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead-ends in pathways, while unknown gene products may catalyze known reactions. New computational methods that analyze data, such as growth phenotypes or gene essentiality, in the context of genome-scale metabolic networks, have been developed to predict these missing reactions or genes likely to fill these knowledge gaps. A growing number of experimental studies are appearing that address these computational predictions, leading to discovery of new metabolic capabilities in the target organism. Gap-filling methods can thus be used to improve metabolic network models while simultaneously leading to discovery of new metabolic gene functions. Biotechnol. Bioeng. 2010;107: 403–412. © 2010 Wiley Periodicals, Inc.

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