%0 Journal Article %1 Orth2010Systematizing %A Orth, Jeffrey D. %A Palsson, Bernhard Ø. %D 2010 %I Wiley Subscription Services, Inc., A Wiley Company %J Biotechnol. Bioeng. %K annotation gaps reconstruction %N 3 %P 403--412 %R 10.1002/bit.22844 %T Systematizing the generation of missing metabolic knowledge %U http://dx.doi.org/10.1002/bit.22844 %V 107 %X 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. \copyright 2010 Wiley Periodicals, Inc.

@article{Orth2010Systematizing, 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. {\copyright} 2010 Wiley Periodicals, Inc.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Orth, Jeffrey D. and Palsson, Bernhard {\O}.}, biburl = {https://www.bibsonomy.org/bibtex/2ad0dddfe83a737d28c50ce9b9f940746/karthikraman}, citeulike-article-id = {7372587}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/bit.22844}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/20589842}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=20589842}, day = 15, doi = {10.1002/bit.22844}, interhash = {514cc5a38ef22d80dc7265b3287204da}, intrahash = {ad0dddfe83a737d28c50ce9b9f940746}, issn = {1097-0290}, journal = {Biotechnol. Bioeng.}, keywords = {annotation gaps reconstruction}, month = oct, number = 3, pages = {403--412}, pmid = {20589842}, posted-at = {2010-07-01 13:35:56}, priority = {2}, publisher = {Wiley Subscription Services, Inc., A Wiley Company}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Systematizing the generation of missing metabolic knowledge}, url = {http://dx.doi.org/10.1002/bit.22844}, volume = 107, year = 2010 }