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

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


Titill: Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics
Höfundur: Bordbar, Aarash
Yurkovich, James T.
Paglia, Giuseppe   orcid.org/0000-0003-4724-6801
Rolfsson, Óttar   orcid.org/0000-0003-4258-6057
Sigurjónsson, Ólafur E.
Palsson, Bernhard O.
Útgáfa: 2017-04-07
Tungumál: Enska
Umfang: 46249
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Háskólinn í Reykjavík
Reykjavik University
Svið: Heilbrigðisvísindasvið (HÍ)
School of Health Sciences (UI)
Deild: Læknadeild (HÍ)
Faculty of Medicine (UI)
Tækni- og verkfræðideild (HR)
School of Science and Engineering (RU)
Birtist í: Scientific Reports;7
ISSN: 2045-2322
DOI: 10.1038/srep46249
Efnisorð: Biochemical networks; Metabolomics; Metabolic pathways; Lífefnafræði; Frumulíffræði; Efnaskipti
URI: https://hdl.handle.net/20.500.11815/268

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Tilvitnun:

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).

Útdráttur:

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.

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Creative Commons Attribution 4.0 International License.

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