Systems and Mathematical Biology

Systems and Mathematical Biology

This team incorporates and develops expertise in all aspects of high-throughput data analysis, network biology, and mathematical modeling of network dynamics. Capabilities for data handling extends across all molecular components and processes of a cell, and this is complemented by strong expertise in the modeling of complex systems behavior. The latter is synergistically approached by exploiting both network-based and purely mathematical strategies. The current thrust is to develop novel methodologies for building data-driven dynamical networks that capture disease progression trajectories. The aim here is to eventually delineate the regulatory nodes that are core to the integrity of such networks. The potential of such nodes as putative drug targets is then evaluated by the Target validation team. Additionally, another strategy employed is to derive data-based genome scale metabolic models to capture disease-specific perturbations in proteo-metabolic flux. Simulation exercises on such networks helps to identify the ‘choke-points’ that regulate these perturbations, which is then complemented by experimental evaluation such choke points as possible drug targets. A combination of mathematical tools are employed in these endeavors. These include differential equations, graph theory, dynamical theorems such as chaos theory, numerical analysis, tools from physics such as percolation theory, and statistical modeling including machine learning.

The Team

  • Samrat Chatterjee (PhD),Assistant Professor
  • Rajat Anand (PhD),Research Associate
  • Ankur Gupta (MSc),Junior Research Fellow
  • Abhijit Paul (MSc),Junior Research Fellow
  • Dipanku Tanu Sarmah (MSc),Junior Research Fellow
  • Suvankar Halder (MSc),Junior Research Fellow
  • Shivam Kumar,Project Assistant