B cell signaling and immune response

Precise understanding of B cell immune response is of utmost importance to the immunologists as well as to the biomedical engineers. B cell antibody generation, for instance, can be modulated in immunotherapeutic strategies like vaccine designs. We have explored the biophysical mechanism of B cell immune synapse formation, which occurs during the course of antigen recognition, using kinetic Monte Carlo simulations. We have recently developed and studied a biophysical model of single molecule diffusion that is synergistic with parallel single molecule tracking experiments of B cell immune synapse formation. We have also developed a very general theoretical model of receptor diffusion during immunological synapse formation that we believe can shed light into the complex problem of immunological synapse formation for both B and T cells. We are currently developing a signaling model of B cells to test the hypothesis that B cell immune synapse formation leads to antigen-affinity discrimination by B cells. A successful investigation can provide crucial insight into the function of B cell immunological synapses.

Apoptotic cell death signaling and diseases

Apoptosis, or genetically programmed cell death, is a crucial cellular process that maintains the balance between life and death in cells. Any disruption of the balance in the apoptotic cell death signaling can lead to diseases ranging from cancer in the case of under-apoptosis to degenerative disorders in over-apoptosis. We have developed a fairly detailed Monte Carlo signaling model of apoptotic cell death signaling. Our results show that two different pathways of apoptosis signaling get activated differentially as the strength of the death stimulus is varied. Our results also explained experimentally observed slow cell death under stress conditions. Currently, we are including a competing growth signaling pathway and studying its effect on the activation of the death-signaling pathway. A probability distribution based approach has been used to characterize stochastic signaling and can be used as a general method to investigate stochastic effects in cellular signaling. We have also developed a simple minimal model of signaling network that can sense and adapt to a fluctuating environment using stochastic fluctuations and can mimic the essential stochastic behavior of a full-scale apoptotic signaling network. We hope this proposed design of the minimal signaling network can guide the further development of biosynthetic circuits and biosensors.

In Silico Experiments

An integral part of my lab research has been development of simulation methods to study receptor-ligand binding mediated cellular signaling processes. We have developed a novel kinetic Monte Carlo method where the time-scale of the biological process under consideration emerges naturally from our simulation. Currently we are developing stochastic differential equations that can describe complex biological processes.