Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation

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dc.contributor.authorCiliberto, Andreaen
dc.contributor.authorCapuani, Fabrizioen
dc.contributor.authorTyson, John J.en
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2016-12-09T21:34:32Zen
dc.date.available2016-12-09T21:34:32Zen
dc.date.issued2007-03-01en
dc.description.abstractIn metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis–Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme–substrate complex (C) is much less than the free substrate concentration (S0). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter–Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter–Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1) it unveils the modular structure of the enzymatic reactions, (2) it suggests a simple algorithm to formulate correct kinetic equations, and (3) contrary to classical Michaelis–Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.en
dc.description.versionPublished versionen
dc.format.extent463 - 472 (10) page(s)en
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.0030045en
dc.identifier.issn1553-734Xen
dc.identifier.issue3en
dc.identifier.urihttp://hdl.handle.net/10919/73631en
dc.identifier.volume3en
dc.languageEnglishen
dc.publisherPLOSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000246191000012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBiochemical Research Methodsen
dc.subjectMathematical & Computational Biologyen
dc.subjectBiochemistry & Molecular Biologyen
dc.subjectXENOPUS-OOCYTE EXTRACTSen
dc.subjectM-PHASE CONTROLen
dc.subjectCELL-CYCLEen
dc.subjectKINETICSen
dc.subjectBISTABILITYen
dc.subjectHYSTERESISen
dc.subjectCASCADESen
dc.titleModeling networks of coupled enzymatic reactions using the total quasi-steady state approximationen
dc.title.serialPLOS Computational Biologyen
dc.typeArticle - Refereeden
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Biological Sciencesen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/University Distinguished Professorsen

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