2026-09-21T00:08:54.034056900Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/32512019-02-06T06:05:53Zcom_10133_3346com_10133_1com_10133_296col_10133_3347col_10133_298
ULethbridge OPUS Repository
supervisor
Roussel, Marc R.
author
Chipindirwi, Simbarashe
2013-04-23T19:22:16Z
2013-04-23T19:22:16Z
2012
https://hdl.handle.net/10133/3251
Gene 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
Gene expression -- Research
Gene expression -- Mathematical models
Stochastic analysis
Markov processes
Dissertations, Academic
Analysis of a simple gene expression model
Thesis
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URL
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application/pdf
CHIPINDIRWI_SIMBARASHE_MSC_2012.pdf
URL
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CHIPINDIRWI_SIMBARASHE_MSC_2012.pdf.txt