Rati Sharma

Computational and Experimental Biophysics

  • Designation : Assistant Professor
  • Phone : +91 755 269 1357
  • Email : rati@iiserb.ac.in

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Academic Details

Assistant Professor at the Dept. of Chemistry, IISER Bhopal (since Feb. 2019)

Postdoctoral research at Harvard University with Prof. Erel Levine (June 2016-Dec. 2018)

Postdoctoral research at Johns Hopkins University with Prof. Elijah Roberts (Dec. 2013 - May 2016)

MS & Ph.D (Int. Ph.D) at IISc, Bangalore with Prof. Binny J. Cherayil (2007-2013)

B.Sc. in Chemistry at Stella Maris College, Chennai (2004-2007)

Profile Details

    The current scientific era is highly conducive to interdisciplinary research as never before have the powers of biology, physics, computer science and statistics come together at such a large scale. Biology, in particular, is going through a revolutionary phase with the conflation of all these different fields. This has become possible largely due to the vast amounts of data being generated in experiments along with developments in microscopy technologies and next generation sequencing. Complementary to these technologies have been advancements in computational power which allow theoretical physics based models to be simulated and tested and bioinformatics which allow data generated in sequencing experiments to be analyzed efficiently. New age statistical techniques like machine learning also help in the analysis and interpretation of data in unique ways.
    The computational and experimental biophysics lab will combine theoretical techniques of non-equilibrium statistical mechanics to make models and predictions along with stress based experiments (such as heat shock, pathogen infection, osmotic stress) performed on the model organism C. elegans (1mm long nematode found commonly in the soil) to answer fundamental questions in science. The lab will be focused on three specific areas. They are:

    • Noise in cellular kinetic pathways: Computer simulations and mathematical models based on statistical mechanics will be used to understand how they affect cellular dynamics.
    • Stress-response dynamics using C. elegans as a model organism and their connection to survival and behavior. This will incorporate the use of microfluidics and imaging through a microscope along with RNA biology.
    • Probing large interaction networks through machine learning. This will involve using and curating large biological public datasets and making predictions.
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