Raman Ganti

Raman Ganti

Experienced researcher with interests in Computational Chemistry, Immunology, and Deep Learning.

Selected Publications

Mechanisms underlying vaccination protocols that may optimally elicit broadly neutralizing antibodies against highly mutable pathogens

Published in Physical Review E, 2021

Highly mutable pathogens such as HIV and SARS-CoV-2 can pose a major challenge to development of effective vaccines because antibodies that are effective against one strain of the virus may not protect against mutant strains. Antibodies that can protect against diverse strains of a mutable pathogen are known as broadly neutralizing. Through the use of stochastic simulation methods, information theory, and analysis of past experimental data, this paper proposes the theoretical conditions that need to be met in order to optimally induce production of broadly neutralizing antibodies via a prime and boost vaccination protocol. In doing so, we connect the Darwinian process of affinity maturation to statistical learning theory.

Recommended citation: Ganti, Raman S., and Arup K. Chakraborty. ``Mechanisms underlying vaccination protocols that may optimally elicit broadly neutralizing antibodies against highly mutable pathogens.' Physical Review E 103.5 (2021): 052408. http://rganti.github.io/files/bnab_PRE.pdf

How the T cell signaling network processes information to discriminate between self and agonist ligands

Published in Proceedings of the National Academy of Sciences of the United States of America, 2020

Information theory was invented to solve the task of sending reliable communication over an unreliable channel. T cells have extremely reliable signal-processing capacity as they sensitively and specifically respond to a few pathogen-derived peptide ligands displayed in the noisy environment of many self-derived ligands presented on the same antigen-presenting cell. In this paper, we used information-theoretic concepts to analyze a computational model of the biochemical steps in the T cell signaling network to understand how key features of the T cell signaling pathway enable discriminatory ability. Our calculations and experiments suggest that T cells superimpose kinetic proofreading steps that must be spatially localized with the receptor and feedback loops to extract reliable information from a noisy environment.

Recommended citation: Ganti, Raman S., et al. ``How the T cell signaling network processes information to discriminate between self and agonist ligands.' Proceedings of the National Academy of Sciences 117.42 (2020): 26020-26030. http://rganti.github.io/files/T_Cell_PNAS.pdf

Hamiltonian Transformation to compute Thermo-osmotic Forces

Published in Physical Review Letters, 2018

If a thermal gradient is applied along a fluid-solid interface, the fluid experiences a thermo-osmotic force. In the steady state, this force is balanced by the gradient of the shear stress. Surprisingly, there appears to be no unique microscopic expression that can be used for computing the magnitude of the thermo-osmotic force. Here we report how, by treating the mass M of the fluid particles as a tensor in the Hamiltonian, we can eliminate the balancing shear force in a nonequilibrium simulation and therefore compute the thermo-osmotic force at simple solid-fluid interfaces. We compare the nonequilibrium force measurement with estimates of the thermo-osmotic force based on computing gradients of the stress tensor. We find that the thermo-osmotic force as measured in our simulations cannot be derived from the most common microscopic definitions of the stress tensor.

Recommended citation: Ganti, Raman, Yawei Liu, and Daan Frenkel. "Hamiltonian transformation to compute thermo-osmotic forces." Physical review letters 121.6 (2018): 068002. http://rganti.github.io/files/Hamiltonian_PhysRevLett.pdf

Molecular Simulation of Thermo-osmotic Slip

Published in Physical Review Letters, 2017

Thermo-osmotic slip—the flow induced by a thermal gradient along a surface—is a well-known phenomenon, but curiously there is a lack of robust molecular-simulation techniques to predict its magnitude. Here, we compare three different molecular-simulation techniques to compute the thermo-osmotic slip at a simple solid-fluid interface. Although we do not expect the different approaches to be in perfect agreement, we find that the differences are barely significant for a range of different physical conditions, suggesting that practical molecular simulations of thermo-osmotic slip are feasible.

Recommended citation: Ganti, Raman, Yawei Liu, and Daan Frenkel. "Molecular simulation of thermo-osmotic slip." Physical review letters 119.3 (2017): 038002. http://rganti.github.io/files/Molecular_Simulation_PhysRevLett.pdf