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dc.contributor.authorReznik, Eden_US
dc.contributor.authorMehta, Pankajen_US
dc.contributor.authorSegrè, Danielen_US
dc.date.accessioned2020-01-29T14:36:29Z
dc.date.available2020-01-29T14:36:29Z
dc.date.issued2013
dc.identifier.citationEd Reznik, Pankaj Mehta, Daniel Segrè. 2013. "Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools." PLoS computational biology, Volume 9, pp. e1003195 - e1003195.
dc.identifier.urihttps://hdl.handle.net/2144/39197
dc.description.abstractStoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.en_US
dc.format.extentp. e1003195 - e1003195en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS computational biology
dc.rightsCopyright: 2013 Reznik et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectLife sbiences & biomedicineen_US
dc.subjectBiochemical research methodsen_US
dc.subjectMathematical & computational biologyen_US
dc.subjectBiochemistry & molecular biologyen_US
dc.subjectBalance analysisen_US
dc.subjectYeasten_US
dc.subjectPhenotypeen_US
dc.subjectNetworksen_US
dc.subjectModelsen_US
dc.subjectOptimizationen_US
dc.subjectEssentialityen_US
dc.subjectRobustnessen_US
dc.subjectOptimalityen_US
dc.subjectAlgorithmsen_US
dc.subjectEscherichia colien_US
dc.subjectGene expression profilingen_US
dc.subjectIntracellular spaceen_US
dc.subjectMetabolic networks and pathwaysen_US
dc.subjectMetabolomicsen_US
dc.subjectModels, biologicalen_US
dc.subjectStatistics, nonparametricen_US
dc.subjectSystems biologyen_US
dc.subjectBiological sciencesen_US
dc.subjectInformation and computing sciencesen_US
dc.subjectMathematical sciencesen_US
dc.subjectBioinformaticsen_US
dc.titleFlux imbalance analysis and the sensitivity of cellular growth to changes in metabolite poolsen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Biologyen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Physicsen_US
dc.identifier.mycv33481


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Copyright:  2013 Reznik et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as Copyright: 2013 Reznik et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.