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Delving deeper : relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis

dc.contributor.authorChristensen, Carl D.en_ZA
dc.contributor.authorHofmeyr, Jan-Hendrik S.en_ZA
dc.contributor.authorRohwer, Johann M.en_ZA
dc.date.accessioned2018-12-13T11:55:57Z
dc.date.available2018-12-13T11:55:57Z
dc.date.issued2018-11-28
dc.identifier.citationChristensen, C. D., Hofmeyr, J. H. S. & Rohwer, J. M. 2018. Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis. PLoS ONE, 13(11):e0207983, doi:10.1371/journal.pone.0207983en_ZA
dc.identifier.issn1932-6203 (online)
dc.identifier.otherdoi:10.1371/journal.pone.0207983
dc.identifier.urihttp://hdl.handle.net/10019.1/105269
dc.descriptionCITATION: Christensen, C. D., Hofmeyr, J. H. S. & Rohwer, J. M. 2018. Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis. PLoS ONE, 13(11):e0207983, doi:10.1371/journal.pone.0207983.en_ZA
dc.descriptionThe original publication is available at http://journals.plos.org/plosoneen_ZA
dc.descriptionPublication of this article was funded by the Stellenbosch University Open Access Fund.en_ZA
dc.description.abstractHigh-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system’s components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions. Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic flux or steadystate concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control. These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.en_ZA
dc.description.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207983
dc.format.extent24 pages : illustrationsen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.subjectMetabolic systemsen_ZA
dc.subjectSymbolic control analysisen_ZA
dc.subjectModels of metabolic pathwaysen_ZA
dc.titleDelving deeper : relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysisen_ZA
dc.typeArticleen_ZA
dc.description.versionPublisher's versionen_ZA
dc.rights.holderAuthors retain copyrighten_ZA


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