Deciphering the dynamics of physiological systems using quantitative and computational approaches

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Physiological systems exhibit varying degrees of complexity and fascinating ways of interacting with their actively-changing environment. The difficulty in investigating these systems experimentally is a major scientific challenge that compel us to employ alternative approaches to analyze them. In my research program, we devise quantitative approaches (adopted from the field of Nonlinear Dynamics and Computational Biology) in parallel with experimental techniques, to study the rich dynamics of neurophysiological and immunological systems. That includes examining electrophysiological and hormonal rhythms in neurons within the brain, analyzing immune-cell response/kinetics and interactions at the single cell and population levels and understanding how subcellular processes (e.g. ion channels, receptors, proteins, etc.) govern the supracellular behavior of both systems within an integrated network. To accomplish this, we design computational tools (e.g. signal processing and machine learning algorithms) to analyze in vivo and in vitro data, and/or develop mathematical models along with quantitative methods to study their behavior. We construct in silico models based on our understanding of the physiological processes regulating them, followed by validating them against experimental data by designing inference-based fitting techniques. The latter is done by performing systematic model comparisons and parameter optimization to determine the most likely model and parameter set(s). Using steady state analysis, slow-fast analysis, numerical techniques and biophysical methods, the models are then investigated to decipher the behavior of physiological systems at the sub/supracellular and (meta)network levels, to provide insights about their general and emergent behavior, to identify causes of abnormalities within them, to test hypotheses and guide new experiments, to make experimentally testable predictions, and to develop technological tools to manipulate them. The models developed also raise intriguing mathematical questions that are tackled either numerically using computational tools that we design, or analytically using methods of nonlinear (stochastic) dynamics.