Analysis of a simple gene expression model

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dc.contributor.authorChipindirwi, Simbarashe
dc.contributor.supervisorRoussel, Marc R.
dc.date.accessioned2013-04-23T19:22:16Z
dc.date.available2013-04-23T19:22:16Z
dc.date.issued2012
dc.degree.levelMasters
dc.descriptionxii, 86 leaves : ill. ; 29 cmen_US
dc.description.abstractGene expression is random owing to the low copy numbers of molecules in a living cell and the best way to study it is by use of a stochastic method, specifically the chemical master equation. The method is used here to derive analytically the invariant probability distributions, and expressions for the moments and noise strength for a simple gene model without feedback. Sensitivity analysis, emphasizing particularly the dependence of the probability distributions, the moments, and noise strength is carried out using Metabolic Control Analysis, which uses control coefficients that measure the response of observables when parameters change. Bifurcation analysis is also carried out. The results show that the number of mRNA molecules follows a hypergeometric probability distribution, and that noise decreases as the number of these molecules increases. Metabolic Control Analysis was successfully extended to genetic control mechanisms, with the obtained control coefficients satisfying a summation theorem. The system undergoes stochastic bifurcations as parameters change.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3251
dc.language.isoen_USen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Chemistry and Biochemistry, c2012en_US
dc.publisher.departmentDepartment of Chemistry and Biochemistryen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectGene expression -- Researchen_US
dc.subjectGene expression -- Mathematical modelsen_US
dc.subjectStochastic analysisen_US
dc.subjectMarkov processesen_US
dc.subjectDissertations, Academicen_US
dc.titleAnalysis of a simple gene expression modelen_US
dc.typeThesisen_US
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