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dc.contributor.authorBordbar A
dc.contributor.authorYurkovich JT
dc.contributor.authorPaglia G
dc.contributor.authorRolfsson O
dc.contributor.authorSigurjónsson ÓE
dc.contributor.authorPalsson BO
dc.date.accessioned2018-10-01T10:23:14Z
dc.date.available2018-10-01T10:23:14Z
dc.date.issued2017
dc.identifier.issn2045-2322
dc.identifier.urihttp://dx.doi.org/10.1038/srep46249
dc.identifier.urihttp://hdl.handle.net/10863/6239
dc.description.abstractThe increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.en_US
dc.language.isoenen_US
dc.rights
dc.titleElucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomicsen_US
dc.typeArticleen_US
dc.date.updated2018-10-01T10:21:54Z
dc.language.isiEN-GB
dc.journal.titleScientific Reports
dc.description.fulltextopenen_US


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