title: Systematizing the generation of missing metabolic knowledge.
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.
authors.affiliation: Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093-0412, USA.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Orth
authors.forename: Bernhard Ø
authors.initials: BØ
authors.lastname: Palsson
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D053858:Metabolic Networks and Pathways; D055442:Metabolome; D049490:Systems Biology