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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6968

Title: Pathway based microarray analysis based on multi-membership gene regulation
Authors: Pavlidis, Stelios
Advisors: Payne, A
Swift, S
Keywords: Bioinformatics
Biochemical pathways
Gene expression analysis
Publication Date: 2011
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
Abstract: Recent developments in automation and novel experimental techniques have led to the accumulation of vast amounts of biological data and the emergence of numerous databases to store the wealth of information. Consequentially, bioinformatics have drawn considerable attention, accompanied by the development of a plethora of tools for the analysis of biological data. DNA microarrays constitute a prominent example of a high-throughput experimental technique that has required substantial contribution of bioinformatics tools. Following its popularity there is an on-going effort to integrate gene expression with other types of data in a common analytical approach. Pathway based microarray analysis seeks to facilitate microarray data in conjunction with biochemical pathway data and look for a coordinated change in the expression of genes constituting a pathway. However, it has been observed that genes in a pathway may show variable expression, with some appearing activated while others repressed. This thesis aims to add some contribution to pathway based microarray analysis and assist the interpretation of such observations, based on the fact that in all organisms a substantial number of genes take part in more than one biochemical pathway. It explores the hypothesis that the expression of such genes represents a net effect of their contribution to all their constituent pathways, applying statistical and data mining approaches. A heuristic search methodology is proposed to manipulate the pathway contribution of genes to follow underlying trends and interpret microarray results centred on pathway behaviour. The methodology is further refined to account for distinct genes encoding enzymes that catalyse the same reaction, and applied to modules, shorter chains of reactions forming sub-networks within pathways. Results based on various datasets are discussed, showing that the methodology is promising and may assist a biologist to decipher the biochemical state of an organism, in experiments where pathways exhibit variable expression.
Description: This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University
Sponsorship: School of Information Systems, Computing and Mathematics, Brunel University
URI: http://bura.brunel.ac.uk/handle/2438/6968
Appears in Collections:Computer Science
School of Information Systems, Computing and Mathematics Theses

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