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

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.