Research in the Grant Lab

The long-term objective of the research in our lab is to develop analytical, computational and database tools that can be used to rapidly identify the chemical structure of compounds in human biofluids. These analytical and computational tools will be useful for:

a.) understanding disease mechanisms,
b.) enhancing the speed of disease diagnosis, and,
c.) enhancing the accuracy of disease prognosis.

Our novel approach is to develop algorithms that predict physical/chemical properties of compounds contained in chemical databases. The physical/chemical properties chosen are those that can be experimentally measured for any unknown compound by HPLC-mass spectrometry. These include retention indices, precursor ion survival curves, collision induced dissociation fragmentation spectra, biological relevance and cross sectional area. Compounds in databases (for example PubChem) whose predicted properties most closely match experimental properties are returned as the most likely candidates for the unknown. We are working to slots validate this system using an in vivo model of multiple sclerosis. By facilitating the rapid structural identification of chemical compounds in clinically relevant biofluids, the tools described here will greatly enhance the ability of metabolomics studies to compliment and synergize other areas of biomedical research and ultimately improve human health.