Research Projects

We propose to develop a mathematical and computational framework for the analysis and modeling of multiplex networks. We propose to develop efficient tools, metrics, and measures to study the dynamic processes on the multiplex network. We will try to extend the algorithms from the spectral graph theory for multiplex networks, which has a wide range of real-world applications. The present proposal first focuses on studying the protein families and their properties. We will create an amino acid network using the threshold method, where positions in proteins have correlated interactions with one another. These positions have multiple interactions with other positions, which can be efficiently represented by a multiplex network. The layers in the multiplex network depend on the physiochemical properties that are selected to create the interaction network. Although all layers come from the same sequence data we expect each layer gives a different characteristic of amino acids network. We will also study the human diseases network as a multiplex network. We will model the disease network with two layers: a genotype layer and a phenotype layer. We will also implement and validate tools on the data taken from other real-world systems such as social networks, world trade networks, and financial systems. An additional goal is to develop an open-source python code for the analysis of multiplex and multilayered networks.