Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.484774
Title: Modelling gene regulatory networks
Author: Ingram, Piers J.
ISNI:       0000 0000 7152 9762
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2008
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Abstract:
This thesis presents the results of mathematical modeling of both individual genes and small networks of genes. The regulation of gene activity is essential for the proper functioning of cells, which employ a variety of molecular mechanisms to control gene expression. Despite this, there is considerable variation in the precise number and timing of protein molecules that are produced. This is because gene expression is fundamentally a noisy process, subject to a number of sources of randomness, including uctuations in metabolite levels, the environment and ampli ed by the very low number of molecules involved. I have developed a probabilistic model of the burst size distribution (the number of proteins produced by the binding of one promoter) of a single gene. Recent experimental data provides excellent agreement with the model, but also reveals limitations of currently available data in determining the origin of variations in expression. A second strand of my work has addressed the dynamics of networks of genes. A network motif is a sub-graph that occurs more often in the network than would be expected by chance. The recurrent presence of certain motifs has been linked to systematic di erences in the functional properties of networks. I have developed models of the possible dynamical behaviour, in particular for the bi-fan motif, a small sub-network with four genes. This motif has been identi ed as the most prevalent in the regulatory networks of both the bacterium Escherichia coli and Saccharaomyces cerevisiae. The results of this work show that the microscopic details of the interactions are of paramount importance, with few inherent constraints on the network dynamics from consideration of network structure alone. This result is relevant to all attempts to model gene networks without su ciently detailed knowledge of the mechanisms of interaction.
Supervisor: Stumpf, Michael ; Stark, Jaroslav Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.484774  DOI: Not available
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