| Title | Systematizing the generation of missing metabolic knowledge. |
| Year of Publication | 2010 |
| Authors | J.D. Orth; B.Ø. Palsson |
| Journal | PLoS Comput Biol |
| 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. |
| URL | PubMed |