Stochastic modelling of gene regulation with microRNAs

Marbà Maya, Martina (2009) Stochastic modelling of gene regulation with microRNAs. MSc(R) thesis, University of Glasgow.

Abstract

The experimental and computational studies of microRNAs, a novel class of gene regulators, discovered relatively recently, is a rapidly growing field. In particular, researchers are focusing on identifying the targets of microRNAs and the roles of microRNAs in post-transcriptional gene regulation; the work presented
in this thesis is a contribution to this field. Here, a range of kinetic models of gene regulation is studied computationally with a view to explore and predict the stochasticity in gene expression and to review the hypothesis that, in addition to reducing levels of target mRNA and proteins, microRNAs tune down the noise in protein output. Previously, it has been shown that other factors such as activation and deactivation rates of gene promoters have a direct effect on the variation of gene expression and the effect of microRNAs on protein output from different promoters is directly studied here. In addition, our methodology allows for a comparison of transcriptional and post-transcriptional modes of gene regulation. Finally, a model is proposed for the study of more realistic problems
of many targets.

The challenging motivation of this thesis is the use of different statistical methods to explore gene expression and noise in protein output. Stochastic numerical simulations have been compared to theoretical analysis, such as the Probability Generating Function Approach and the method of matrices developed by Gadgil et al, showing similar results for the magnitude of noise in different systems. The Langevin Equation and Tau-Leaping methods (for which Matlab codes are developed here) are shown to be excellent approximations to the Gillespie Algorithm.

• Item Type: Thesis (MSc(R))
• Qualification Level: Masters
• Keywords: gene, microRNA, stochastic, post-transcriptional, Probability Generating Function Approach, Gadgil et al, Gillespie, Tau-Leaping, Langevin Equation
• Subjects: H Social Sciences > HA Statistics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Supervisor's Name: Khanin, Dr. Raya
• Date of Award: 2009
• Depositing User: Mrs MARTINA MARBÀ
• Unique ID: glathesis:2009-844
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 05 Jun 2009
• Last Modified: 10 Dec 2012 13:27
• URI: https://theses.gla.ac.uk/id/eprint/844