Computational analysis of regulatory networks using genome-scale data
type: CreativeWork
description: Living cells are the product of gene expression programs involving regulated transcription of thousands of genes. In single-celled organisms such as S. Cerevisiae regulatory networks respond to the external environment, optimizing the cell at a given time for survival in this environment. Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol. Central dogma of molecular biology describes a key assumption of molecular biology, namely, that each gene in the DNA molecule carries the information needed to construct one protein, which, acting as an enzyme, controls one chemical reaction in the cell. Any step during this information flow can be modulated, from the DNA-RNA transcription step to post-translational modification of a protein. The main stages where gene expression is regulated are chromatin domains, Transcription, Posttranscriptional modification, RNA transport, Translation, mRNA degradation and Post-translational modifications. Understanding how the network is manipulated at these different regulatory levels in a co-ordinated way is a major challenge for molecular biologists. Due to the advent of high throughput technology, it has now become feasible to study regulatory networks at the genomic level. Thus the main aim of this thesis is to characterize regulatory networks using genome-scale data. The first step in gene regulation is transcriptional regulation. The association of transcription factors with genes across a genome can be described as a transcriptional regulatory network. Transcriptional networks is widely studied mainly because of availability of data. The expression data publicly available is growing exponentially and is also becoming available for a variety of species. Analyzing these genome-wide expression datasets to build reliable regulatory networks still remains a challenge due to two main problems. Firstly the noisy nature of the data makes it hard to distinguish signal f…
abstract: Living cells are the product of gene expression programs involving regulated transcription of thousands of genes. In single-celled organisms such as S. Cerevisiae regulatory networks respond to the external environment, optimizing the cell at a given time for survival in this environment. Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol. Central dogma of molecular biology describes a key assumption of molecular biology, namely, that each gene in the DNA molecule carries the information needed to construct one protein, which, acting as an enzyme, controls one chemical reaction in the cell. Any step during this information flow can be modulated, from the DNA-RNA transcription step to post-translational modification of a protein. The main stages where gene expression is regulated are chromatin domains, Transcription, Posttranscriptional modification, RNA transport, Translation, mRNA degradation and Post-translational modifications. Understanding how the network is manipulated at these different regulatory levels in a co-ordinated way is a major challenge for molecular biologists. Due to the advent of high throughput technology, it has now become feasible to study regulatory networks at the genomic level. Thus the main aim of this thesis is to characterize regulatory networks using genome-scale data. The first step in gene regulation is transcriptional regulation. The association of transcription factors with genes across a genome can be described as a transcriptional regulatory network. Transcriptional networks is widely studied mainly because of availability of data. The expression data publicly available is growing exponentially and is also becoming available for a variety of species. Analyzing these genome-wide expression datasets to build reliable regulatory networks still remains a challenge due to two main problems. Firstly the noisy nature of the data makes it hard to distinguish signal f…
author: Joshi, Anagha Madhusudan
language: eng
keywords: Biology and Life Sciences
date published: 2009
date modified: 2026-07-24T02:23:00Z
provider: Ghent University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Ghent University. Faculty of Sciences

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property ID: Handle
value: http://hdl.handle.net/1854/LU-3007419