Computational Modeling and Analysis of Gene Regulatory Interaction Network for Metabolic Disorder: a Bioinformatics Approach

Abstract

In this study, we generate a PPI network and co-regulatory networks to understand the mechanisms of metabolic disorder more clearly. This study also analyzes the relevance of genes that are responsible for Cardiovascular (CVD), Obesity (OBS), Type 2 diabetes (T2D) and Hypertension (HT). It also showed the common genome among CVD, OBS, T2D, and HT. Using Bioinformatics approaches, drugs are possible to design. For this study gene was collected from NCBI (National Center for Biotechnology Information) using R language. Primarily, 7197 genes were found for CVD, 3140 are for OBS, 3283 genes were for T2D and 2237 are for HT which were responsible for all species. Among those genes, 12 top-weighted common genes were selected for this research. Using these liable common genes, a protein-protein interaction network (PPI) and a regulatory interaction network were constructed. The PPI network shows the interaction among those genes. And the regulatory interaction network defines the direct and indirect connection among selected genes. The PPI network will help to design more reliable drug targets.