Tanmay Mitra (University of Oxford) – Adaptive Dynamics in Biological Systems Across Scales: From Cellular Memory to Pharmaceutical & Epidemiologic Non-pharmaceutical Interventions

Biological systems are remarkable in their ability to adapt to dynamic environments, encode memory, and learn, spanning scales from intricate signaling pathways within cells to coordinated responses in populations. At the subcellular level, we demonstrated that long-lived memory and non-associative learning can originate in intra-cellular signaling networks from distinct timescales of operation among signaling components, forming the basis for signal integration. In the cellular level, our agent-based modeling revealed that T cells integrate signals from sub-threshold antigenic interactions to form immune synapses. Furthermore, we showed that the enhanced flexibility of bispecific antibodies, designed to simultaneously bind targets on T cells and antigen-presenting cancer or host cells, can reduce their therapeutic potency and synapse formation propensity, with crucial translational implications for cancer and autoimmune therapies. Expanding to the cell-cell interaction network, we simulated germinal centers, the sites of extensive T cell-B cell crosstalk and the formation of memory B cells and antibody-secreting cells, using a state-of-the-art agent-based model. We revealed how germinal centers adapt B cell selection to balance immune diversity with specificity. We also studied regulation of autoimmunity in germinal centers by tingible body macrophages and T follicular regulatory cells. In the level of organism-organism interactions, we led data-driven adaptive modeling integrated with healthcare usage during the SARS-CoV-2 outbreak, influencing key political decisions on non-pharmaceutical interventions globally, including advising former German Chancellor Angela Merkel. A central focus of my research, thus, has been to investigate emergent phenomena across biological scales using an interdisciplinary approach and derive translational insights for disease interventions.