Stochastic Modeling of Gene Expression and Post-transcriptional Regulation

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dc.contributor.authorJia, Taoen
dc.contributor.committeechairKulkarni, Rahul V.en
dc.contributor.committeememberHeflin, James R.en
dc.contributor.committeememberPark, Kyungwhaen
dc.contributor.committeememberPleimling, Michel J.en
dc.contributor.departmentPhysicsen
dc.date.accessioned2014-03-14T20:14:38Zen
dc.date.adate2011-08-19en
dc.date.available2014-03-14T20:14:38Zen
dc.date.issued2011-07-21en
dc.date.rdate2011-08-19en
dc.date.sdate2011-08-01en
dc.description.abstractStochasticity is a ubiquitous feature of cellular processes such as gene expression that can give rise to phenotypic differences for genetically identical cells. Understanding how the underlying biochemical reactions give rise to variations in mRNA/protein levels is thus of fundamental importance to diverse cellular processes. Recent technological developments have enabled single-cell measurements of cellular macromolecules which can shed new light on processes underlying gene expression. Correspondingly, there is a need for the development of theoretical tools to quantitatively model stochastic gene expression and its consequences for cellular processes. In this dissertation, we address this need by developing general stochastic models of gene expression. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. Furthermore, given that the underlying processes are intrinsically stochastic, cellular regulation must be designed to control the`noise' in order to adapt and respond to changing environments. Another focus of this dissertation is to develop and analyze stochastic models of post-transcription regulation. The analytical solutions of the models proposed provide insight into the effects of different mechanisms of regulation and the role of small RNAs in fine-tunning the noise in gene expression. The results derived can serve as building blocks for future studies focusing on regulation of stochastic gene expression.en
dc.description.degreePh. D.en
dc.identifier.otheretd-08012011-170602en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08012011-170602/en
dc.identifier.urihttp://hdl.handle.net/10919/28483en
dc.publisherVirginia Techen
dc.relation.haspartJia_Tao_D_2011.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjecttranscriptional burstingen
dc.subjectregulatory sRNAen
dc.subjectpost-transcriptional regulationen
dc.subjectstochastic gene expressionen
dc.subjectqueueing theoryen
dc.titleStochastic Modeling of Gene Expression and Post-transcriptional Regulationen
dc.typeDissertationen
thesis.degree.disciplinePhysicsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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