Sriram Chandrasekaran, Ph.D.

Fighting drug resistance using AI: We are developing mechanistic AI tools (e.g. INDIGO, MAGENTA, CARAMEL) to design drug combinations with enhanced potency and reduced potential for developing resistance. We also study pathogen metabolism and pathogen-immune interactions to discover new synergistic antibiotics against M. tuberculosis, S. aureus and other pathogens. Systems biology methods for simulating metabolic regulation: Our lab is developing new modeling tools to simulate the activity of thousands of metabolic reactions in a human or microbial cell, providing a unique systems perspective on metabolic regulation. We have applied the methods that we developed (e.g. PROM, DFA, GEMINI, RECON8D, and ASTRIX) to understand microbial, stem-cell, cancer, and brain metabolism using omics datasets.

A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations. Cell Reports Methods 2025. Inferring Metabolic Objectives and Tradeoffs in Single Cells During Embryogenesis, Cell Systems, 2025. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS Nexus, 2022.