Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.684261
Title: The role of extrinsic noise in biomolecular information processing systems : an in silico analysis
Author: Lenive, Oleg
ISNI:       0000 0004 5920 6492
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
The intrinsic stochasticity of biomolecular systems is a well studied phe- nomenon. Less attention has been paied to other sources of variability, so called extrinsic noise. While the precise definition of extrinsic noise de- pends on the system in question, it affects all cells and its significance has been demonstrated experimentally. Information theory provides a rigorous mathematical framework for quan- tifying both the amount of information available to a signalling system and its ability to transmit this information. Intracellular signal transduction re- mains a relatively unexplored frontier for the application of information theory. In this thesis, we rely on a metric called mutual information to quantify in- formation flow in models of biochemical signalling systems. After briefly discussing the theoretical background and some of the practical difficulties of estimating mutual information in Chapter 2, we apply it in the context of simplified models of intracellular signalling, referred to as motifs. Using a comprehensive set of two-node motifs we explore the effects of extrinsic noise, model parameters and various combinations of interaction, on the system's ability to transmit information about an input signal, repre- sented by a telegraph process. Our results illustrate the importance of the system's response time and demonstrate a trade-off in transmitting infor- mation about the current state of the input or its average intensity over a period of time. In Chapter 4, we address the problem of determining the magnitude of ex- trinsic noise in the presence of intrinsic stochasticity. Using the Approximate Bayesian Computation - sequential Monte Carlo algorithm, together with published experimental data, we infer parameters describing extrinsic noise in a model of E. coli gene expression. Lastly, in Chapter 5, we construct and analyse models of bacterial two- component signalling, bringing together insights gleaned from earlier work. The results show how the abundances of different molecular species in the system may transmit information about the input signal despite its stochas-tic nature and considerable variation in the numbers of protein molecules present.
Supervisor: Krishnan, J. ; Stumpf, Michael Sponsor: Biotechnology and Biological Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.684261  DOI: Not available
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