Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541759
Title: Exploiting network-based approaches for understanding gene regulation and function
Author: Janga, Sarath Chandra
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2010
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Abstract:
It is increasingly becoming clear in the post-genomic era that proteins in a cell do not work in isolation but rather work in the context of other proteins and cellular entities during their life time. This has lead to the notion that cellular components can be visualized as wiring diagrams composed of different molecules like proteins, DNA, RNA and metabolites. These systems-approaches for quantitatively and qualitatively studying the dynamic biological systems have provided us unprecedented insights at varying levels of detail into the cellular organization and the interplay between different processes. The work in this thesis attempts to use these systems or network-based approaches to understand the design principles governing different cellular processes and to elucidate the functional and evolutionary consequences of the observed principles. Chapter 1 is an introduction to the concepts of networks and graph theory summarizing the various properties which are frequently studied in biological networks along with an overview of different kinds of cellular networks that are amenable for graph-theoretical analysis, emphasizing in particular on transcriptional, post-transcriptional and functional networks. In Chapter 2, I address the questions, how and why are genes organized on a particular fashion on bacterial genomes and what are the constraints bacterial transcriptional regulatory networks impose on their genomic organization. I then extend this one step further to unravel the constraints imposed on the network of TF-TF interactions and relate it to the numerous phenotypes they can impart to growing bacterial populations. Chapter 3 presents an overview of our current understanding of eukaryotic gene regulation at different levels and then shows evidence for the existence of a higher-order organization of genes across and within chromosomes that is constrained by transcriptional regulation. The results emphasize that specific organization of genes across and within chromosomes that allowed for efficient control of transcription within the nuclear space has been selected during evolution. Chapter 4 first summarizes different computational approaches for inferring the function of uncharacterized genes and then discusses network-based approaches currently employed for predicting function. I then present an overview of a recent high-throughput study performed to provide a 'systems-wide' functional blueprint of the bacterial model, Escherichia coli K-12, with insights into the biological and evolutionary significance of previously uncharacterized proteins. In Chapter 5, I focus on post-transcriptional regulatory networks formed by RBPs. I discuss the sequence attributes and functional processes associated with RBPs, methods used for the construction of the networks formed by them and finally examine the structure and dynamics of these networks based on recent publicly available data. The results obtained here show that RBPs exhibit distinct gene expression dynamics compared to other class of proteins in a eukaryotic cell. Chapter 6 provides a summary of the important aspects of the findings presented in this thesis and their practical implications. Overall, this dissertation presents a framework which can be exploited for the investigation of interactions between different cellular entities to understand biological processes at different levels of resolution.
Supervisor: Mohan, Madan Babu Sponsor: Medical Research Council ; Cambridge Commonwealth Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.541759  DOI: Not available
Keywords: Transcriptional regulation ; Networks ; Function prediction ; Systems biology ; Post-transcriptional regulation ; Genomics
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