Jayanth Krishnan

The paper constructs a genome-scale metabolic model for E. faecium TX0016 that accurately predicts auxotrophies and carbon source utilization, and compares its gene essentiality predictions to E. faecalis data.

The paper uses pangenome analysis of Escherichia coli to reveal the genetic basis of metabolic reactions and their evolutionary dynamics.

This study analyzes 164 Pseudomonas putida strains to reveal metabolic and genetic diversity, curating GEMs and identifying strain-specific adaptations for biotechnology applications.

This paper uses machine learning to analyze transcriptomic data from Yarrowia lipolytica under various industrial stresses, identifying regulatory modules and their responses to environmental changes.

The paper demonstrates that Escherichia coli can utilize extracellular electron transfer via 2-hydroxy-1,4-naphthoquinone to support anaerobic growth, revealing a new energy metabolism pathway.