Comparative Strategy for the Statistical & Network based Analysis of Biological Networks

Abstract

The basic life sustaining biological processes in a biological system are performed in systematic manner with the help of different biological networks related to biomolecules such as proteins, genes, metabolites etc. The various bio-molecular networks named Protein-Protein Interaction Network [PPIN], Gene Regulatory Network [GRN], Metabolic Network [MBN] & Signal Transduction Network [STN] interacts with each other to build complex network of interactions. The malfunctions in these networks can manifest a disease phenotype. Understanding the science of complex networks is vital in exploiting the relationship between several biological activities which can unveil the perception over the complex disease states & the root causes behind it. The science of complex networks can give insights about alterations in these interaction networks that lead to phenotypes. In the midst of large set of biological network databases, the real challenge lies in the reconstruction & inference operations mainly due to the high scale & complexity of known & unknown interactions. The more accurate & sheer network construction along with proper analysis can lead to personalized medicine & quality treatment by identifying the culprit biomolecules or biological pathways. Computational analysis of complex network provide fast, reliable & cost effective methodologies in early disease detection, prediction of co-occurring diseases & interactions, drug designing, drug side-effects etc. In this work we focused on three distinct biological networks named PPIN, GRN & MBN along with associated knowledge sources, and measures for network analysis, methodologies & comparison / analysis of them.