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Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.

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Authors
Bordbar, Aarash
Yurkovich, James T
Paglia, Giuseppe
Rolfsson, Ottar
Sigurjónsson, Ólafur E
Palsson, Bernhard O
Issue Date
2017-04-07

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Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. 2017, 7:46249 Sci Rep
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 (13)C 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.
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Efst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field.
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384226/pdf/srep46249.pdf
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Archived with thanks to Scientific reports
ae974a485f413a2113503eed53cd6c53
10.1038/srep46249
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