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
PubMed Date2010 Oct 15
KeywordsBiomedical Research, Metabolic Networks and Pathways, Metabolome, Systems Biology

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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