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(*) Summary: All cellular processes are highly regulated to optimise their function in the face of extrinsic and intrinsic noise. Multiple designs of regulation with positive and negative feedbacks are observed in intracelllular biochemical pathways and cell-cell interactions in multi-cell systems (e.g., tissues). Even though much of the details of cellular processes are known experimentally, the emergent functional dynamics of different designs of regulation in pathways and multi-cell systems, and their implications in cellular behaviour are largely overlooked for differences in their organisational scales. I will discuss our work on model pathways with different regulatory designs, and the emergent spatiotemporal dynamics exhibited by interacting cells having such regulated pathways. I will then show how, designing, modelling, and experimentally validating a small circuit, developed based on naturally occurring intracellular biochemical pathways, can give valuable insights into cellular functions.

(**) Biodata: Professor Somdatta Sinha's research interests cover several areas in Theoretical biology, Nonlinear Dynamics and Complex Systems with a view to understand the logic and design of biological processes. Her group analyses the evolution and maintenance of spatiotemporal organization in biological systems spanning multiple space-time scales - from ecological to genetic. The primary approach is mathematical modelling, but they also perform computational analysis of genomes and protein sequences to extract structural, functional and evolutionary information. Along with finding general principles of controlling dynamics and synchronisation in biological systems, they specifically model regulation in biochemical pathways, evolution of pathways, and collective behaviour in cells. They use networks to describe protein structure-function and have focussed on those functional changes which yield insignificant variations in three-dimensional structures. One of the major focus of her group has been ecological and epidemiological modelling. They use genomic analysis to population-based mathematical and statistical models to understand the evolution and prevalence of infectious diseases (HIV and Malaria).