arXiv subjects

Bhanwar Lal Puniya

Publications and source records attributed to Bhanwar Lal Puniya.

3 recordsLinked to original sources

arXiv2026

MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models

Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use demands substantial computational expertise and careful coordination of multi-step analyses. We developed MechAInistic to lower this barrier and enable researchers to ask complex biological questions in natural language. Harnessing large language models, MechAInistic is a multi-agent system organized around an Architect-Reviewer pattern that transforms a natural-language question into an executable, model-grounded workflow and generates a structured report. The system supports a variety of tasks, including pathway comparison, perturbation analysis, drug-target exploration, and literature-grounded interpretation across paired metabolic model states. We developed and evaluated MechAInistic using two paired immune-cell metabolic-model use cases for therapeutic hypothesis generation. For Naive B cells from rheumatoid arthritis (RA) paired with healthy controls, MechAInistic identified mitochondrial metabolic rewiring and nominated Devimistat/CPI-613 as an investigational OGDH-centered hypothesis. In a paired CD4+ Th17 cell study from multiple sclerosis (MS) and healthy controls, the same workflow identified NADP-dependent isocitrate dehydrogenase as the optimal single target and proposed ivosidenib as an FDA-approved repurposing candidate. Together, these results show that MechAInistic converts natural-language biological questions into executable, model-grounded workflows for traceable therapeutic hypothesis generation.

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arXiv2023

COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools. We developed COMO, a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases, and disease data to aid drug discovery. COMO can be installed as a Docker image and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for multi-omics integration of bulk and single-cell RNA-seq, microarrays, and proteomics to develop context-specific metabolic models. Using public databases, open-source solutions for model construction, and a streamlined approach for predicting repurposable drugs, COMO empowers researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict 25 and 23 metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies.

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arXiv2015

Systems Perturbation Analysis of a Large Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model's components was perturbed under both loss-of-function and gain-of-function mutations. We identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Lastly, our results suggest that systematic perturbation analyses of large-scale computational models may serve as an approach to prioritize and assess signal transduction components in order to identify novel drug targets in complex disorders.

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