Extraction of genetic network from microarray data using Bayesian framework

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dc.contributor.advisorBessant, Conrad
dc.contributor.advisorSetford, S.
dc.contributor.authorKumuthini, Judit
dc.date.accessioned2023-04-13T12:33:43Z
dc.date.available2023-04-13T12:33:43Z
dc.date.issued2007-04
dc.description.abstractThe aim of the work described in this thesis was to develop novel methods for the extraction of gene regulatory networks (GRN) from gene expression data, and use these methods to capture previously unknown relationships between genes in specific biological applications. This has been accomplished through the application of Bayesian Networks (BN) through minimum description length (MDL) and taboo search for parameter and structure learning respectively to three large scale microarray datasets from Saccharomyeces cerevisae, Escherichia coli and human stem cells. The application of BNs for modelling the well characterised yeast cell cycle demonstrated the efficacy of the techniques employed. Using the cDNA microarray data from the yeast cell cycle project by Spellman et a l (1998), this study succeeded in extracting many biologically plausible genetic relationships, which were supported by evidence from publicly available genome and literature databases. Two novel knowledge extraction techniques were applied; Target Node (TN) analysis and learning through simulation. Further, it was demonstrated how the addition of prior knowledge to the extracted network can improve the network structure extracted purely from experimental data. The second part of this thesis demonstrated how the BN approach could be adapted to a data set of very high dimensionality, specifically data from a 54,634 probe array used to monitor human adipose tissue. Genetic networks extracted included insulin receptor (IR) and Fatty acid binding proteins (FABP) families that play key roles in fatty acid uptake, transport, and metabolism In the final part of this thesis, the genome-wide GRNs of a prokaryotic expression system were extracted from novel oligo cDNA microarray data from E-coli K12 to identify metabolic stress responsive genes during recombinant protein production. Also, detailed analysis of known metabolic stress related genes and the genes that are directly or indirectly associated in the GRN were used to establish possible markers for host system exhaustion. In conclusion, the BN methods developed proved to be a powerful and effective means of extracting GRNs in a variety of applications.en_UK
dc.description.coursenamePhDen_UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19456
dc.language.isoenen_UK
dc.rights© Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.
dc.titleExtraction of genetic network from microarray data using Bayesian frameworken_UK
dc.typeThesisen_UK

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PhD, EngD and MSc by research theses (Cranfield Health)