Ganesh A Viswanathan

Biography

Postdoctoral Fellowship, Mount Sinai School of Medicine, Manhattan, New York, USA PhD in Chemical Engineering, University of Houston, Houston, USA M.S in Chemical Engineering, Indian Institute of Science, Bangalore, India B.Tech in Chemical and Electrochemical Engineering, Central Electrochemical Research Institute, Karaikudi, India

Research Interest

Noise propagation during TNFα signaling in mammalian systems Tumor necrosis factor – (TNFα), an important cytokine, is involved in mammalian innate and adaptive immune regulation and is used in cancer treatment. Dysregulation and overproduction of TNFα have been implicated in many human diseases. Signal transduction, that is a series of molecular events in immune cells triggered by TNFα is known to be both pro-apoptotic and pro-survival. The extent of propagation and distribution in the TNFα signaling pathway may decide the fate of a cell. Strategies to control noise propagation during TNFα signaling can significantly help in engineering a cell to exhibit a specific and favorable response. We conduct systems biology based high-throughput experimentation, and mathematical modeling and simulations to identify the entities and the associated mechanisms that may govern cellular response in the presence of noise following TNFα stimulation. In order to facilitate this study, using manual curation process we have initiated construction of a comprehensive TNFα signaling network. Construction and analysis of biological networks Cellular decision making and responses are orchestrated by complex molecular networks. As an aid to experimental and theoretical biological research, it is useful to distill information presented in various primary experimental literature and other databases relevant to these network into knowledgebases. We are currently developing strategies to construct, store and disseminate biological signaling networks. We are also developing novel computational biology based approaches for reconstruction of metabolic and regulatory networks. We use various systems biology approaches to analyze these constructed networks. Pattern formation in packed-bed reactors Packed-bed reactors (PBRs) are the workhorse of chemical industry. Spatiotemporal temperature patterns may form in PBRs during an exothermic catalytic reaction. These patterns can pose severe safety issues and are detrimental to the reactor performance. It is of practical importance to find out why and under what conditions spatiotemporal patterns form in PBRs. We conduct mathematical modeling and use non-linear analysis to understand the mechanisms and detect the conditions that may lead to pattern formation. Knowledge of the mechanisms and the conditions is useful for improved design and operation of PBRs.