dc.description.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. | nb_NO |