Raghunathan Rengaswamy

The paper develops a computational framework to analyze robustness, plasticity, evolvability, and canalisation in three-node genetic circuits by simulating and classifying over 190 million circuits.

This paper introduces CausalFlux, a method modeling bidirectional feedback between genes and metabolites, showing improved prediction of reaction fluxes and gene essentiality compared to one-way feedback models.

This study introduces PIVOT, a multi-omic machine learning model that classifies genes as tumor suppressors, oncogenes, or neutral, improving prediction accuracy for BRCA, LUAD, and COAD datasets.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper investigates how silver nanoparticles induce oxidative stress in gut bacteria, increasing folic acid production, which reduces colon cancer cell viability.

The paper develops an integrated computational model to explore how diet and gut microbiota interact in autism, showing probiotics reduce oxidative stress and microbiome-diet interactions affect host metabolism.