{"id":{"repo_id":"glasgow","oai_identifier":"oai:theses.gla.ac.uk:844"},"canonical_url":"https://search.dev.ndltd.org/etd/glasgow/oai:theses.gla.ac.uk:844","repository":{"repo_id":"glasgow","name":"University of Glasgow","base_url":"https://theses.gla.ac.uk/cgi/oai2"},"display":{"title":"Stochastic modelling of gene regulation with microRNAs","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Marbà Maya, Martina"],"institution":"University of Glasgow","degree_name":null,"degree_level":"MSc(R)","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009","date_published":"2009","updated_at":"2026-07-24T02:23:34Z","subjects":["HA Statistics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Marbà Maya, Martina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2009"]},{"key":"dc:date.issued","label":"Date","values":["2009"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Glasgow"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://theses.gla.ac.uk/844/"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://gla.on.worldcat.org/oclc/1547488240"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["MSc(R)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["HA Statistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://theses.gla.ac.uk/844/1/2009MarbaMayaMSc.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Stochastic modelling of gene regulation with microRNAs"]}]}],"canonical_facts":{"dc:creator":["Marbà Maya, Martina"],"dc:date":["2009"],"dc:date.issued":["2009"],"dc:description.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."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://theses.gla.ac.uk/844/1/2009MarbaMayaMSc.pdf"],"dc:language":["en"],"dc:publisher.institution":["University of Glasgow"],"dc:relation.isreferencedby":["https://theses.gla.ac.uk/844/"],"dc:relation.isreferencedby.uri":["https://gla.on.worldcat.org/oclc/1547488240"],"dc:subject":["HA Statistics"],"dc:title":["Stochastic modelling of gene regulation with microRNAs"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["MSc(R)"]},"updated_at":"2026-07-24T02:23:34Z"}