Marbà Maya, Martina
Stochastic modelling of gene regulation with microRNAs.
MSc(R) thesis, University of Glasgow.
Full text available as:
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.
Actions (login required)