Mathematical and Computational Methods for Metabolic Engineering
We are developing new computational strategies that use metabolomics datasets to improve the accuracy of strains designed in silico by predicting intracellular metabolite concentrations and using machine learning strategies to infer regulatory interactions.
Engineering Bacterial Biosensors
We are developing a novel medical test based on bacterial sensors that respond to defined micronutrient levels. Such a test would be cheap, convenient, and allow on-site diagnosis of micronutrient deficiencies in the populations most at risk.
Exploring Crabtree Effect in Yeast via Metabolomics
We are exploring the major metabolic difference between species of yeast known as the Crabtree effect through the use of metabolomics analysis to improve our understanding of yeast evolution, inform metabolic engineering strategies, and discover new insights about the Crabtree effect that may apply to other organisms.