The metabolic profiles generated by our experiments will allow us to determine specific metabolites and pathways that are altered by biological perturbations, granting us further insight into cancer metabolism.
Stem cell metabolism alters significantly as cells differentiate down various lineages. Comparing metabolism along time courses of several kinds of differentiation highlights how and when metabolic profiles of stem cells changes as they develop to different types.
Initial studies show that CoMet (computational Metabolomics) has predicted some anti-proliferative metabolites when tested on Jurkat cells. This work proposes to improve CoMet’s predictive accuracy by integrating GCxGC-MS measurements of metabolite levels into the model.
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.
Small molecule microarrays of yeast metabolites and assays coupled with gas chromatography analysis will provide a high-throughput platform for qualitatively identifying protein-metabolite binding pairs.
Mathematical modeling, machine learning techniques, and Bayesian networks will be used to analyze and integrate different types of “-omic” data sets from non-human primate model systems and their corresponding malaria parasites.
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.