Network topologies unraveling randomness and preserved patterns in disease complexome

Abstract

The inside of a cell is turbulent, as numerous proteins, metabolites, tiny molecules, and DNA strands wind around each other to accompany thousands of interactions. Each of these molecules are a set of functioning assistants to other molecules that helps in proper cellular signalling. Hence, the overall functioning of these molecules is of jugglers play in the cell orchestra. The functioning of the cell can take on a very different character if even a single member of this molecular orchestra starts to behave strangely. Some disease states are a consequence of one or many of such flaws in molecular interactions that eventually result in the altered dynamics of expressions of the differential molecules. Understanding the relationship between these altered molecular interactions is one of the greatest challenges in modern biology and medicine. Further, wider availability of biological information from technological advancements capture minute details of the molecular interactions through variety of CHIP and RNA modifications, -omics technology as well as other bundle of NGS experiments. Utilising this vast information, rapid advancements in both experimental and theoretical techniques have been performed in recent years. However, heterogeneity exhibited by various disease models (cancer, diabetes, neural diseases, etc.) increases the complexity of the already complicated cellular pathways and functioning networks and thus, requires development of novel tools to counter the diseasome at the systems level. Development of statistical tools, may prove to be highly potent in addressing such complex disease models. Network Science along with spectral graph theory and random matrix theory has shown its tremendous success in a wide variety of disciplines, being it as diverse as the human brain, the world wide web, scientific collaborations, communications and power systems engineering to molecular and population biology. This framework has helped in uncovering the complexity at the fundamental level enabling us tohave a global view of the diseasome. Constructing molecule interaction networks such as Protein-Protein Interaction (PPI) networks for different diseases provide a unique platform for understanding the altered interactions between the normal and the diseased tissues from the information and literature available with various bioinformatics resources. This thesis investigates various structural and spectral properties of various cancers and diabetes mellitus II for their normal and disease counterpart. These studies further help in improving our current knowledge of molecular associations in disease models in a time efficient and cost effective manner. Thus, employing such a technique may further lead to advancements in disease diagnosis, prognosis and identification of novel drug targets for cancer therapy. This novel approach further provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalised medicine.