The overall cellular functions in a cell, an outcome of tightly regulated inter and intracellular connectivity can be studied as protein-protein interaction (PPI) networks. Their effects are tangible: protein-protein interactions can alter kinetic properties of other proteins, create new binding sites, act as mediators in biochemical pathways, inactivate or degrade other proteins and even change specificities of binding partners. With the burst of data generated pertaining to protein-protein interactions via high-throughput experimental methods, PPIs are better understood in the context of network principles. Mathematically, PPIs can be represented as a graph consisting of a set of nodes and edges. Proteins form the nodes and interacting protein-pairs are connected by an edge. PPI networks have hitherto been explored by looking at the perturbations in networks caused by node removal, essentially meaning the removal of proteins and their interactions from the network. This approach, although useful in understanding the statistical properties, lacks in accounting for the effect of residue perturbations such as mutations and conformational change in individual proteins, on the overall pathway or cellular PPI network. The proposed project aims to bridge this gap by adding a structure-based network layer on top of each node of the PPI network to connect the protein atomic structure and its variations with the PPI network. This will lead to each PPI being represented as a network of structure networks (NoSN). Challenging systems like the mammalian circadian have been increasingly studied for their role in different biological processes and diseases, understanding the molecular mechanisms of regulation of circadian rhythm is pertinent. Most of the current work in the field of circadian biology in India has been focused on the organismal level or identification of effector proteins. This leaves a huge gap between the identification of effector proteins and the molecular mechanism explaining the phenotype. A NoSN (multilayer) network biology approach can be utilized to understand the system-wide effect of residue perturbations and its effect on the overall network stability of PPINs by applying various graph theoretical approaches.
saysayani11/Multilayer_network_modelling
Generalizing multilayer networks with a focus on network of structure networks
Python