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dc.contributor.authorStanford, Natalie J.en_US
dc.contributor.authorLubitz, Timoen_US
dc.contributor.authorSmallbone, Kieranen_US
dc.contributor.authorKlipp, Eddaen_US
dc.contributor.authorMendes, Pedroen_US
dc.contributor.authorLiebermeister, Wolframen_US
dc.date.accessioned2018-10-16T16:41:24Z
dc.date.available2018-10-16T16:41:24Z
dc.date.issued2013-09-14en_US
dc.identifier.othere79195en_US
dc.identifier.urihttp://hdl.handle.net/10919/85383
dc.description.abstractThe quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherPLOSen_US
dc.rightsCreative Commons Attribution 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.titleSystematic Construction of Kinetic Models from Genome-Scale Metabolic Networksen_US
dc.typeArticle - Refereeden_US
dc.description.versionPeer Revieweden_US
dc.title.serialPLOS ONEen_US
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0079195en_US
dc.identifier.volume8en_US
dc.identifier.issue11en_US
dc.type.dcmitypeTexten_US
dc.identifier.pmid24324546en_US
dc.identifier.eissn1932-6203en_US


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Creative Commons Attribution 4.0
License: Creative Commons Attribution 4.0