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Measurement and modeling of transcriptional noise in the cell cycle regulatory network

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dc.contributor.authorBall, David A.en
dc.contributor.authorAdames, Neil R.en
dc.contributor.authorReischmann, Nadineen
dc.contributor.authorBarik, Debashisen
dc.contributor.authorFranck, Christopher T.en
dc.contributor.authorTyson, John J.en
dc.contributor.authorPeccoud, Jeanen
dc.contributor.departmentBiological Sciencesen
dc.contributor.departmentStatisticsen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.contributor.departmentInstitute for Critical Technology and Applied Scienceen
dc.date.accessioned2016-12-09T21:41:20Zen
dc.date.available2016-12-09T21:41:20Zen
dc.date.issued2013-10-01en
dc.description.abstractFifty years of genetic and molecular experiments have revealed a wealth of molecular interactions involved in the control of cell division. In light of the complexity of this control system, mathematical modeling has proved useful in analyzing biochemical hypotheses that can be tested experimentally. Stochastic modeling has been especially useful in understanding the intrinsic variability of cell cycle events, but stochastic modeling has been hampered by a lack of reliable data on the absolute numbers of mRNA molecules per cell for cell cycle control genes. To fill this void, we used fluorescence in situ hybridization (FISH) to collect single molecule mRNA data for 16 cell cycle regulators in budding yeast, Saccharomyces cerevisiae. From statistical distributions of single-cell mRNA counts, we are able to extract the periodicity, timing, and magnitude of transcript abundance during the cell cycle. We used these parameters to improve a stochastic model of the cell cycle to better reflect the variability of molecular and phenotypic data on cell cycle progression in budding yeast.en
dc.description.versionPublished versionen
dc.format.extent3203 - 3218 (16) page(s)en
dc.identifier.doihttps://doi.org/10.4161/cc.26257en
dc.identifier.issn1538-4101en
dc.identifier.issue19en
dc.identifier.urihttp://hdl.handle.net/10919/73643en
dc.identifier.volume12en
dc.languageEnglishen
dc.publisherLandes Bioscienceen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000327381700014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCell Biologyen
dc.subjectcell cycleen
dc.subjectstochastic modelingen
dc.subjectgene expression noiseen
dc.subjectSaccharomycesen
dc.subjectcerevisiaeen
dc.subjectsingle mRNA FISHen
dc.subjectMESSENGER-RNA DECAYen
dc.subjectMITOTIC-EXIT CONTROLen
dc.subjectGENE-EXPRESSIONen
dc.subjectBUDDING YEASTen
dc.subjectSACCHAROMYCES-CEREVISIAEen
dc.subjectSIZE CONTROLen
dc.subjectPROTEINen
dc.subjectSTOCHASTICITYen
dc.subjectFEEDBACKen
dc.subjectDYNAMICSen
dc.titleMeasurement and modeling of transcriptional noise in the cell cycle regulatory networken
dc.title.serialCell Cycleen
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/Science/Statisticsen
pubs.organisational-group/Virginia Tech/University Distinguished Professorsen

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