Systematizing the generation of missing metabolic knowledge.

TitleSystematizing the generation of missing metabolic knowledge.
Publication TypeJournal Article
Year of Publication2010
AuthorsOrth JD, Palsson BØ
JournalBiotechnology and bioengineering
Volume107
Issue3
Pagination403-12
PubMed Date2010 Oct 15
ISSN1097-0290
KeywordsBiomedical Research, Metabolic Networks and Pathways, Metabolome, Systems Biology
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.

Alternate JournalBiotechnol. Bioeng.
PubMed ID20589842

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