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Integrative Systems Biology Approaches for Analyzing High-Throughput Data Applications to modeling of gene regulatory networks

Jayavelu, Naresh Doni
Doctoral thesis
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URI
http://hdl.handle.net/11250/283645
Date
2015
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Abstract
Systems biology is relatively new and interdisciplinary field developed to study and

understand the complex behavior of biological systems at systems level rather than

at its individual elements. This is achieved by integrating experimental data and

known systems knowledge in an iterative manner. Emergence of high-throughput

omic technologies changed the fate of experimental biological research, generated

large quantities of data at low cost. Therefore new systems biology, bioinformatics

models, algorithms are necessary to utilize those generated data and study the

system.

Gene regulatory networks play a key role in cellular processes such as cell

proliferation, differentiation, cell cycle and signal transduction in response to

physiological and environmental perturbations, for instance cues. Transcription

factors and microRNAs are two central drivers/regulators in gene regulation

process. Predicting dynamic interactions between these regulators and genes can

provide basis for understanding mechanisms that underlie in the complex diseases

as cancers.

The main focus of this thesis is to analyze the high-throughput microarray gene

expression data (static and dynamic data) and integrate with extracted biological

data from literature to construct and model the gene regulatory networks. Network

component analysis (NCA) has been used as central modeling tool along with other

bioinformatics tools.

The work presented in this thesis articulates the series of integrative systems biology

and bioinformatics approaches applied to high-throughput microarray data (both

static and dynamic) to study different aspects of gene transcriptional regulation in

different organisms. The first contribution presents the framework to remove the

noise and separate the true biological signals from the raw microarray data. The next

demonstrates the iterative sub network component analysis (ISNCA), a frame work developed to reconstruct the large scale gene regulatory networks using standard

NCA algorithms inherently. Next chapter presents a framework for constructing the

active sub networks (a group of active TFs and genes) at different time ranges based

on NCA results. Further analysis of these constructed networks showed structural

variation in their network organization and involved in distinct biological processes.

In addition, NCA approach is extended to study microRNAs regulated network in

breast cancer. Next two chapters present the application of NCA to plant microarray

data treated with cold and heat stresses respectively.

Lastly, a review article about one of the high-throughput omics technologies,

Metabolomics and its applications with special focus on gastric cancer is presented.
Publisher
NTNU
Series
Doctoral thesis at NTNU;2015:68

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