Nirav Bhatt

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.

The paper introduces SPECTRA, a method to reconstruct metabolic networks from multi-omics data at large-scale, improving speed and reaction inclusion across cancer cell lines, microbial reconstructions, and synthetic gut microbiota.

The paper develops an interpretable machine learning framework to decode genotype-phenotype maps in yeast, identifying causal genes and pathway enrichments from QTL data.

The paper uses genome-scale metabolic models to identify causal reactions influenced by SNP-SNP interactions affecting yeast sporulation efficiency.

This study uses CFD modeling to evaluate impeller designs for plant cell fermentation, finding that the setric impeller provides a low-shear environment with high cell-lift capabilities for Viola odorata cultures.

Localgini improves context-specific metabolic models by using gene expression inequality to derive more comprehensive core reactions.

This paper develops a reactive species module for genome-scale metabolic models to identify metabolic targets in colorectal cancer and highlights ferroptosis as a therapeutic target.

This paper introduces a data reconciliation framework to estimate thermodynamically consistent Gibbs free energies from experimental data, improving accuracy over existing methods.