Our Research Focus

The research theme of our lab is to understand the mechanism of biological phenomena using mathematical and computational tools. One of our research areas is to capture underlying mechanisms of disease progression and identify potential drug targets. It can then be validated and further studied by the experimental biologists. To achieve this, we first derive information from big data using different available systems biology tools like network analysis, clustering, etc., or by developing new tools like a bi-clustering algorithm, connecting temporal data, identifying node sensitivity under random perturbations, etc. We capture the hidden mechanisms and targets using differential equation-based kinetic models from this information. We solve those dynamical models analytically and numerically to obtain sensitive parameters responsible for the system's different dynamical behavior. Another research area in which our lab works is identifying molecular signatures associated with a disease and its progression. We used them for the development of diagnostic and prognostic models. We build models based on machine learning (ML) algorithms.