dc.contributor |
Háskóli Íslands |
dc.contributor |
University of Iceland |
dc.contributor |
Háskólinn í Reykjavík |
dc.contributor |
Reykjavik University |
dc.contributor.author |
Bordbar, Aarash |
dc.contributor.author |
Yurkovich, James T. |
dc.contributor.author |
Paglia, Giuseppe |
dc.contributor.author |
Rolfsson, Óttar |
dc.contributor.author |
Sigurjónsson, Ólafur E. |
dc.contributor.author |
Palsson, Bernhard O. |
dc.date.accessioned |
2017-05-15T15:40:23Z |
dc.date.available |
2017-05-15T15:40:23Z |
dc.date.issued |
2017-04-07 |
dc.identifier.citation |
Bordbar, A. et al. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci. Rep. 7, 46249; doi: 10.1038/srep46249 (2017). |
dc.identifier.issn |
2045-2322 |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/268 |
dc.description.abstract |
The 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. |
dc.description.sponsorship |
This work was supported by the National Heart Lung and Blood Institute (R43HL123074 and R43HL127843), the European Research Council (232816), and the U.S. Department of Energy (DE-SC0008701). |
dc.format.extent |
46249 |
dc.language.iso |
en |
dc.publisher |
Springer Nature |
dc.relation.ispartofseries |
Scientific Reports;7 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Biochemical networks |
dc.subject |
Metabolomics |
dc.subject |
Metabolic pathways |
dc.subject |
Lífefnafræði |
dc.subject |
Frumulíffræði |
dc.subject |
Efnaskipti |
dc.title |
Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics |
dc.type |
info:eu-repo/semantics/article |
dcterms.license |
Creative Commons Attribution 4.0 International License. |
dc.description.version |
Peer Reviewed |
dc.description.version |
Ritrýnt tímarit |
dc.identifier.journal |
Scientific Reports |
dc.identifier.doi |
10.1038/srep46249 |
dc.relation.url |
https://www.nature.com/articles/srep46249 |
dc.contributor.department |
Læknadeild (HÍ) |
dc.contributor.department |
Faculty of Medicine (UI) |
dc.contributor.school |
Heilbrigðisvísindasvið (HÍ) |
dc.contributor.school |
School of Health Sciences (UI) |
dc.contributor.school |
Tækni- og verkfræðideild (HR) |
dc.contributor.school |
School of Science and Engineering (RU) |