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Strategies for representing metabolic pathways within biochemical systems theory: Reversible pathways

dc.contributor.authorSorribas, Alberten_US
dc.contributor.authorSavageau, Michael A.en_US
dc.date.accessioned2006-04-07T20:48:12Z
dc.date.available2006-04-07T20:48:12Z
dc.date.issued1989-06en_US
dc.identifier.citationSorribas, Albert, Savageau, Michael A. (1989/06)."Strategies for representing metabolic pathways within biochemical systems theory: Reversible pathways." Mathematical Biosciences 94(2): 239-269. <http://hdl.handle.net/2027.42/27912>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6VHX-45FKF2J-4F/2/b4dcf331b7708747f49fddec4ed2b392en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/27912
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=2520170&dopt=citationen_US
dc.description.abstractThe search for systematic methods to deal with the integrated behavior of complex biochemical systems has over the past two decades led to the proposal of several theories of biochemical systems. Among the most promising is biochemical systems theory (BST). Recent comparisons of this theory with several others that have recently been proposed have demonstrated that all are variants of BST and share a common underlying formalism. Hence, the different variants can be precisely related and ranked according to their completeness and operational utility. The original and most fruitful variant within BST is based on a particular representation, called an S-system (for synergistic and saturable systems), that exhibits many advantages not found among alternative representations. Even within the preferred S-system representation there are options, depending on the method of aggregating fluxes, that become especially apparent when one considers reversible pathways. In this paper we focus on the paradigm situation and clearly distinguish the two most common strategies for generating an S-system representation. The first is called the "reversible" strategy because it involves aggregating incoming fluxes separately from outgoing fluxes for each metabolite to define a net flux that can be positive, negative, or zero. The second is the "irreversible" strategy, which involves aggregating forward and reverse fluxes through each reaction to define a net flux that is always positive. This second strategy has been used almost exclusively in all variants of BST. The principal results of detailed analyses are the following: (1) All S-system representations predict the same changes in dependent concentrations for a given change in an independent concentration. (2) The reversible strategy is superior to the irreversible on the basis of several criteria, including accuracy in predicting steady-state flux, accuracy in predicting transient responses, and robustness of representation. (3) Only the reversible strategy yields a representation that is able to capture the characteristic feature of amphibolic pathways, namely, the reversal of nets flux under physiological conditions. Finally, the results document the wide range of variation over which the S-system representation can accurately predict the behavior of intact biochemical systems and confirm similar results of earlier studies [Voit and Savageau, Biochemistry 26: 6869-6880 (1987)].en_US
dc.format.extent1769843 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleStrategies for representing metabolic pathways within biochemical systems theory: Reversible pathwaysen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbsecondlevelEcology and Evolutionary Biologyen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Microbiology and Immunology, The University of Michigan, Ann Arbor, Michigan 48109-0620, USAen_US
dc.contributor.affiliationumDepartment of Microbiology and Immunology, The University of Michigan, Ann Arbor, Michigan 48109-0620, USAen_US
dc.identifier.pmid2520170en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/27912/1/0000333.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0025-5564(89)90066-7en_US
dc.identifier.sourceMathematical Biosciencesen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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