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Stochastic simulation of enzyme-catalyzed reactions with disparate timescales

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dc.contributorVirginia Techen
dc.contributor.authorBarik, Debashisen
dc.contributor.authorPaul, Mark R.en
dc.contributor.authorBaumann, William T.en
dc.contributor.authorCao, Yangen
dc.contributor.authorTyson, John J.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.contributor.departmentMechanical Engineeringen
dc.contributor.departmentBiological Sciencesen
dc.contributor.departmentComputer Scienceen
dc.date.accessed2014-02-05en
dc.date.accessioned2014-02-26T19:10:05Zen
dc.date.available2014-02-26T19:10:05Zen
dc.date.issued2008-10-01en
dc.description.abstractMany physiological characteristics of living cells are regulated by protein interaction networks. Because the total numbers of these protein species can be small, molecular noise can have significant effects on the dynamical properties of a regulatory network. Computing these stochastic effects is made difficult by the large timescale separations typical of protein interactions (e. g., complex formation may occur in fractions of a second, whereas catalytic conversions may take minutes). Exact stochastic simulation may be very inefficient under these circumstances, and methods for speeding up the simulation without sacrificing accuracy have been widely studied. We show that the "total quasi-steady-state approximation'' for enzyme-catalyzed reactions provides a useful framework for efficient and accurate stochastic simulations. The method is applied to three examples: a simple enzyme-catalyzed reaction where enzyme and substrate have comparable abundances, a Goldbeter-Koshland switch, where a kinase and phosphatase regulate the phosphorylation state of a common substrate, and coupled Goldbeter-Koshland switches that exhibit bistability. Simulations based on the total quasi-steady-state approximation accurately capture the steady-state probability distributions of all components of these reaction networks. In many respects, the approximation also faithfully reproduces time-dependent aspects of the fluctuations. The method is accurate even under conditions of poor timescale separation.en
dc.description.sponsorshipNational Institutes of Health GM078989en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBarik, Debashis; Paul, Mark R.; Baumann, William T.; et al. "Stochastic simulation of enzyme-catalyzed reactions with disparate timescales," Biophysical Journal 95(8), 3563-3574 (2008); doi: 10.1529/biophysj.108.129155en
dc.identifier.doihttps://doi.org/10.1529/biophysj.108.129155en
dc.identifier.issn0006-3495en
dc.identifier.urihttp://hdl.handle.net/10919/25775en
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0006349508785011en
dc.language.isoenen
dc.publisherCell Pressen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSteady-state approximationen
dc.subjectCoupled chemical-reactionsen
dc.subjectXenopus-oocyteen
dc.subjectExtractsen
dc.subjectM-phase controlen
dc.subjectSystemsen
dc.subjectKineticsen
dc.titleStochastic simulation of enzyme-catalyzed reactions with disparate timescalesen
dc.title.serialBiophysical Journalen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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