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dc.contributor.authorRolfsson, Ottar
dc.contributor.authorPalsson, Bernhard Ø
dc.contributor.authorThiele, Ines
dc.date.accessioned2015-06-05T14:29:59Zen
dc.date.available2015-06-05T14:29:59Zen
dc.date.issued2011en
dc.identifier.citationBMC Syst Biol. 2011, 5:155en
dc.identifier.issn1752-0509en
dc.identifier.pmid21962087en
dc.identifier.doi10.1186/1752-0509-5-155en
dc.identifier.urihttp://hdl.handle.net/2336/556480en
dc.description.abstractMetabolic network reconstructions formalize our knowledge of metabolism. Gaps in these networks pinpoint regions of metabolism where biological components and functions are "missing." At the same time, a major challenge in the post genomic era involves characterisation of missing biological components to complete genome annotation.
dc.description.abstractWe used the human metabolic network reconstruction RECON 1 and established constraint-based modelling tools to uncover novel functions associated with human metabolism. Flux variability analysis identified 175 gaps in RECON 1 in the form of blocked reactions. These gaps were unevenly distributed within metabolic pathways but primarily found in the cytosol and often caused by compounds whose metabolic fate, rather than production, is unknown. Using a published algorithm, we computed gap-filling solutions comprised of non-organism specific metabolic reactions capable of bridging the identified gaps. These candidate solutions were found to be dependent upon the reaction environment of the blocked reaction. Importantly, we showed that automatically generated solutions could produce biologically realistic hypotheses of novel human metabolic reactions such as of the fate of iduronic acid following glycan degradation and of N-acetylglutamate in amino acid metabolism.
dc.description.abstractThe results demonstrate how metabolic models can be utilised to direct hypotheses of novel metabolic functions in human metabolism; a process that we find is heavily reliant upon manual curation and biochemical insight. The effectiveness of a systems approach for novel biochemical pathway discovery in mammals is demonstrated and steps required to tailor future gap filling algorithms to mammalian metabolic networks are proposed.
dc.description.sponsorshipinfo:eu-repo/grantAgreement/EC/FP7/232816 info:eu-repo/grantAgreement/EC/FP7/249261en
dc.language.isoenen
dc.publisherBioMed Central Ltden
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/249261en
dc.relation.urlhttp://dx.doi.org/ 10.1186/1752-0509-5-155en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224382/en
dc.rightsopenAccessen
dc.subject.meshAlgorithmsen
dc.subject.meshComputational Biologyen
dc.subject.meshHumansen
dc.subject.meshMetabolic Networks and Pathwaysen
dc.subject.meshModels, Biologicalen
dc.subject.meshSystems Biologyen
dc.titleThe human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions.en
dc.typearticleen
dc.contributor.departmentAddresses: [Show the Organization-Enhanced name(s)] [ 1 ] Univ Iceland, Ctr Syst Biol, IS-101 Reykjavik, Iceland [Show the Organization-Enhanced name(s)] [ 2 ] Univ Iceland, Fac Ind Engn Mech Engn & Comp Sci, IS-101 Reykjavik, Icelanden
dc.identifier.journalBMC systems biologyen
dc.rights.accessOpen Access - Opinn aðganguren
refterms.dateFOA2018-09-12T15:15:33Z
html.description.abstractMetabolic network reconstructions formalize our knowledge of metabolism. Gaps in these networks pinpoint regions of metabolism where biological components and functions are "missing." At the same time, a major challenge in the post genomic era involves characterisation of missing biological components to complete genome annotation.
html.description.abstractWe used the human metabolic network reconstruction RECON 1 and established constraint-based modelling tools to uncover novel functions associated with human metabolism. Flux variability analysis identified 175 gaps in RECON 1 in the form of blocked reactions. These gaps were unevenly distributed within metabolic pathways but primarily found in the cytosol and often caused by compounds whose metabolic fate, rather than production, is unknown. Using a published algorithm, we computed gap-filling solutions comprised of non-organism specific metabolic reactions capable of bridging the identified gaps. These candidate solutions were found to be dependent upon the reaction environment of the blocked reaction. Importantly, we showed that automatically generated solutions could produce biologically realistic hypotheses of novel human metabolic reactions such as of the fate of iduronic acid following glycan degradation and of N-acetylglutamate in amino acid metabolism.
html.description.abstractThe results demonstrate how metabolic models can be utilised to direct hypotheses of novel metabolic functions in human metabolism; a process that we find is heavily reliant upon manual curation and biochemical insight. The effectiveness of a systems approach for novel biochemical pathway discovery in mammals is demonstrated and steps required to tailor future gap filling algorithms to mammalian metabolic networks are proposed.


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