Publications by Bharath Narayanan

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Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite ...

The increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenize ...

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

Dataset supporting the results presented in the paper entitled "Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production" by Narayanan, Jiang & Wang et al (https://doi.org/10.1016/j.ymben.2025.06.008). Dataset ...

Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with ...

Bharath Narayanan

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Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Dataset - Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production

Rational strain design with minimal phenotype perturbation