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Principal Investigator: Metz, Thomas O
Institute Receiving Award Battelle Pacific Northwest Laboratories
Location Richland, WA
Grant Number U2CES030170
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 01 Sep 2018 to 30 Jun 2022
DESCRIPTION (provided by applicant): OVERALL SUMMARY The capability to chemically identify thousands of metabolites and other chemicals in clinical samples will revolutionize the search for environmental, dietary, and metabolic determinants of disease. By comparison to near-comprehensive genetic information, comparatively little is understood of the totality of the human metabolome, largely due to insufficiencies in molecular identification methods. Through innovations in computational chemistry and advanced ion mobility separations coupled with mass spectrometry, we propose to overcome a significant, long standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of metabolites without relying on data from analysis of authentic chemical standards. A paradigm shift in metabolomics, we will use gas-phase molecular properties that can be both accurately predicted computationally and consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome without the need for authentic chemical standards. The outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) transforming metabolomics science by enabling consideration of the totality of the human metabolome through optimized identification of currently unidentifiable molecules, eventually reaching hundreds of thousands of molecules, and (ii) developing standardized computational tools and analytical methods to increase the national capacity for biomedical researchers to identify metabolites quickly and accurately. This work is significant because it enables comprehensive and confident chemical measurement of the metabolome. This work is innovative because it utilizes an integrated quantum-chemistry and machine learning computational pipeline to accurately predict physical-chemical properties of metabolites coupled to measurements.
Science Code(s)/Area of Science(s) Primary: 77 - Metabolomics Technology
Secondary: 03 - Carcinogenesis/Cell Transformation
Publications See publications associated with this Grant.
Program Officer David Balshaw
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