Dr. Samrat Chatterjee, a gold medalist in MSc Mathematics from Jadavpur University, obtained his PhD in Mathematics from the Indian Statistical Institute, Kolkata. His PhD work involved application of various mathematical tools in the field of population ecology. In particular, he focused on studying the role of migratory birds in spreading disease, and also on the relationship between phytoplankton and zooplankton. Subsequently Samrat moved to Italy for his postdoctoral research, to join the Department of Mathematics of Torino University under the supervision of Prof. Ezio Venturino, a renowned expert in numerical analysis. This stint enabled him to gain strong expertise in the numerical simulation of complex systems, using computer applications such as MATLAB and MAPLE. He then returned to India and joined the International Centre for Genetic Engineering and Biotechnology (ICGEB) as a Research Scientist. Here he focused on understanding disease progression dynamics in the metabolic syndrome. The challenges associated with this project prompted Samrat to shift his research interests from ecology to cell biology. Over time, he added new analytical tools such as network analysis and pattern recognition algorithms to his armory for studying high-throughput biological data. His research expertise now covers a wide spectrum of theoretical tools that span from large-scale network analysis, to small-scale mathematical models. He joined DDRC in 2013 with the aim to contribute his expertise in Systems Biology for the identification of novel drug targets. His broad approach involves interrogation of multi-scale ‘omics’ data through a combination of advanced mathematical and computational tools. Samrat has built a mathematical and computational group drawing people from diverse backgrounds. This group is dedicated to developing new mathematical algorithms for dissecting complex high-throughput data, and also the application of mathematical models for delineating disease mechanisms. His team also exploits machine-learning techniques for building models that are capable of predicting disease progression trajectories.

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