A Hybrid Model of Mammalian Cell Cycle Regulation

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dc.contributor.authorSinghania, R.en
dc.contributor.authorSramkoski, R. M.en
dc.contributor.authorJacobberger, J. W.en
dc.contributor.authorTyson, John J.en
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2016-12-09T21:36:20Zen
dc.date.available2016-12-09T21:36:20Zen
dc.date.issued2011-02-01en
dc.description.abstractThe timing of DNA synthesis, mitosis and cell division is regulated by a complex network of biochemical reactions that control the activities of a family of cyclin-dependent kinases. The temporal dynamics of this reaction network is typically modeled by nonlinear differential equations describing the rates of the component reactions. This approach provides exquisite details about molecular regulatory processes but is hampered by the need to estimate realistic values for the many kinetic constants that determine the reaction rates. It is difficult to estimate these kinetic constants from available experimental data. To avoid this problem, modelers often resort to `qualitative' modeling strategies, such as Boolean switching networks, but these models describe only the coarsest features of cell cycle regulation. In this paper we describe a hybrid approach that combines the best features of continuous differential equations and discrete Boolean networks. Cyclin abundances are tracked by piecewise linear differential equations for cyclin synthesis and degradation. Cyclin synthesis is regulated by transcription factors whose activities are represented by discrete variables (0 or 1) and likewise for the activities of the ubiquitin-ligating enzyme complexes that govern cyclin degradation. The discrete variables change according to a predetermined sequence, with the times between transitions determined in part by cyclin accumulation and degradation and as well by exponentially distributed random variables. The model is evaluated in terms of flow cytometry measurements of cyclin proteins in asynchronous populations of human cell lines. The few kinetic constants in the model are easily estimated from the experimental data. Using this hybrid approach, modelers can quickly create quantitatively accurate, computational models of protein regulatory networks in cells.en
dc.description.versionPublished versionen
dc.format.extent10 pagesen
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1001077en
dc.identifier.issn1553-734Xen
dc.identifier.issue2en
dc.identifier.urihttp://hdl.handle.net/10919/73635en
dc.identifier.urlhttp://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001077en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherPLOSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000287698700012&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.subjectANAPHASE-PROMOTING COMPLEXen
dc.subjectRESTRICTION POINTen
dc.subjectBUDDING YEASTen
dc.subjectCYTOMETRYen
dc.subjectDIVISIONen
dc.subjectMITOSISen
dc.subjectIMMUNOFLUORESCENCEen
dc.subjectOSCILLATIONSen
dc.subjectPROTEOLYSISen
dc.subjectHYSTERESISen
dc.titleA Hybrid Model of Mammalian Cell Cycle Regulationen
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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