Bhanwar Lal Puniya

MechAInistic uses a multi-agent system guided by LLMs to generate auditable hypotheses from genome-scale metabolic models, identifying potential drug targets and metabolic rewiring in disease states.

AutoRNAseq provides an automated bulk RNA-seq analysis pipeline to ensure consistent and reproducible processing across experiments.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper identifies 68 potential drug targets for three autoimmune diseases by integrating genome-scale metabolic models with gene expression data and in vitro validation.

This paper introduces a comprehensive mechanistic model of the human immune system as a digital twin blueprint, integrating immune cells, secretory factors, and disease conditions to simulate immune responses.

The paper develops a multi-scale model of CD4+ T cells integrating multiple modeling approaches to predict switch-like and oscillatory behaviors in inflammatory responses.