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Computational analysis of regulatory networks using genome-scale data

Anagha Madhusudan Joshi UGent (2009)
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 from noise and secondly it provides only a static view of the dynamic system. Despite these disadvantages it is becoming popular amongst experimentalists as it provides a genome-wide view of the cellular system and secondly such experiments are increasingly becoming cost effective. Thus we focus mainly to develop methods inferring transcriptional regulatory networks. Despite the large bias towards characterizing transcriptional regulation, regulatory programs at other regulatory levels are also being studied at least in model organisms like S. Cerevisiae. Thus regulatory programs at post-transcriptional and post-translational levels also need to be unravelled as and when the data becomes available. Although we still struggle to get the most out of isolated large-scale datasets, viii OBJECTIVES developing strategies to integrate and jointly model these data is proving to be even more challenging. One of the strategies to understand these complex regulatory networks is to identify the simplest units of commonly used network architecture. We imagine that these simple units, or network motifs, provide specific regulatory capacities such as positive and negative feedback loops. The frequency with which cells use individual motifs reveals the regulatory strategies that were selected during evolution. These motifs can be assembled into network structures that help explain how a complex gene expression program is regulated. Thus a goal of this thesis is also to find such fundamental regulatory units which make the complex regulatory patterns.
Please use this url to cite or link to this publication:
author
promoter
UGent
organization
year
type
dissertation (monograph)
subject
pages
XI, 142 pages
publisher
Ghent University. Faculty of Sciences
place of publication
Ghent, Belgium
defense location
Zwijnaarde : Technologiepark (FSVM building)
defense date
2009-12-16 10:30
language
English
UGent publication?
yes
classification
D1
additional info
dissertation consists of copyrighted material
copyright statement
I have transferred the copyright for this publication to the publisher
id
3007419
handle
http://hdl.handle.net/1854/LU-3007419
date created
2012-10-05 11:53:38
date last changed
2012-10-08 11:21:24
@phdthesis{3007419,
  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 from noise and secondly it provides only a static view of the dynamic system. Despite these disadvantages it is becoming popular amongst experimentalists as it provides a genome-wide view of the cellular system and secondly such experiments are increasingly becoming cost effective. Thus we focus mainly to develop methods inferring transcriptional regulatory networks. Despite the large bias towards characterizing transcriptional regulation, regulatory programs at other regulatory levels are also being studied at least in model organisms like S. Cerevisiae. Thus regulatory programs at post-transcriptional and post-translational levels also need to be unravelled as and when the data becomes available. Although we still struggle to get the most out of isolated large-scale datasets, viii OBJECTIVES developing strategies to integrate and jointly model these data is proving to be even more challenging. One of the strategies to understand these complex regulatory networks is to identify the simplest units of commonly used network architecture. We imagine that these simple units, or network motifs, provide specific regulatory capacities such as positive and negative feedback loops. The frequency with which cells use individual motifs reveals the regulatory strategies that were selected during evolution. These motifs can be assembled into network structures that help explain how a complex gene expression program is regulated. Thus a goal of this thesis is also to find such fundamental regulatory units which make the complex regulatory patterns.},
  author       = {Joshi, Anagha Madhusudan},
  language     = {eng},
  pages        = {XI, 142},
  publisher    = {Ghent University. Faculty of Sciences},
  school       = {Ghent University},
  title        = {Computational analysis of regulatory networks using genome-scale data},
  year         = {2009},
}

Chicago
Joshi, Anagha Madhusudan. 2009. “Computational Analysis of Regulatory Networks Using Genome-scale Data”. Ghent, Belgium: Ghent University. Faculty of Sciences.
APA
Joshi, A. M. (2009). Computational analysis of regulatory networks using genome-scale data. Ghent University. Faculty of Sciences, Ghent, Belgium.
Vancouver
1.
Joshi AM. Computational analysis of regulatory networks using genome-scale data. [Ghent, Belgium]: Ghent University. Faculty of Sciences; 2009.
MLA
Joshi, Anagha Madhusudan. “Computational Analysis of Regulatory Networks Using Genome-scale Data.” 2009 : n. pag. Print.