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

• DOI: 10.1038/srep46249
• Publication Date: 2017-04-07T11:25:24Z
• Authors: Aarash Bordbar; James T. Yurkovich; Giuseppe Paglia; Ottar Rolfsson; Ólafur E. Sigurjó...; Bernhard O. Palsson
• Keywords: Blood Platelets; 570; Erythrocytes; 1.1 Normal biological development and functioning; Saccharomyces cerevisiae; Medical Biochemistry and Metabolomics; Models, Biological; Article; Industrial Biotechnology; Workflow; Algorithms; Blood Platelets; Erythrocytes; Escherichia coli; Humans; Markov Chains; Metabolic Networks and Pathways; Metabolome; Monte Carlo Method; Saccharomyces cerevisiae; Workflow; Metabolomics; Models, Biological; 03 medical and health sciences; Underpinning research; Models; Escherichia coli; Humans; Metabolomics; 0303 health sciences; Biomedical and Clinical Sciences; Biological Sciences; Biological; Markov Chains; 3. Good health; Chemical Sciences; Metabolome; Monte Carlo Method; Algorithms; Metabolic Networks and Pathways

Abstract The increasing availability of metabolomics data necessitates novel methods for deeper analysis and interpretation. We present a flux balance method that allows the computation dynamic intracellular metabolic changes at cellular scale through integration time-course absolute quantitative metabolomics. This approach, termed “unsteady-state analysis” (uFBA), is applied to four systems: three one steady-state as negative control. uFBA FBA predictions are contrasted, found be more accurate in predicting states red blood cells, platelets, Saccharomyces cerevisiae . Notably, only predicts stored cells metabolize TCA intermediates regenerate important cofactors, such ATP, NADH, NADPH. These pathway usage were subsequently validated 13 C isotopic labeling cells. Utilizing data, provides an predict physiology systems.