Bharath Narayanan

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

The paper uses kinetic models to guide metabolic engineering of S. cerevisiae strains, resulting in improved p-coumaric acid production with minimal impact on growth rate.

This paper introduces RENAISSANCE, a generative machine learning framework that accurately characterizes intracellular metabolic states by parameterizing kinetic models using omics data and experimental observations.

This paper introduces a framework for rational strain design in E. coli that minimizes phenotype perturbation using mixed-integer linear programming and kinetic models to improve anthranilate production.