Novel Approaches to Spectral Prediction and Spectral Deconvolution for Metabolomics

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My research is focused on a field of science called metabolomics. Metabolomics involves the study of the metabolome - the complete collection of metabolites found in biological systems. Metabolomics provides important insights into metabolism and biochemistry. It has led to important discoveries that are changing our understanding of disease, nutrition and ecology. These discoveries are leading to new drugs, safer foods and better environmental monitoring. Metabolomics uses advanced technologies such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy to comprehensively characterize as much of the metabolome, as rapidly as possible. Unfortunately, the current tools available for metabolomics are proving inadequate. Indeed, less than 2% of the 10,000+ signals collected in many MS-based metabolomics studies are identifiable. Furthermore, many metabolomics methods require weeks of tedious, manual work. In other words, metabolomics is not particularly comprehensive nor is it very high throughput. The limited throughput and comprehensiveness in metabolomics appears to be due to a lack of appropriate reference spectra of pure compound standards (to aid in compound identification) and the lack of automated tools for using these reference spectra to the observed spectra of biological mixtures. Unfortunately, there are insufficient resources to experimentally create or collect all the necessary reference spectra needed for the metabolomics community. For this NSERC proposal I intend to build on recent discoveries made in my lab, with regard to spectral prediction and analysis, to make metabolomics faster, more comprehensive and more useful to researchers in plant, food and environmental science. In particular, we will combine experimentally acquired data (NMR and MS) collected in my lab with several advanced computational techniques to pursue 4 research objectives. The first objective involves collecting reference NMR and MS spectra for a targeted set of ~500 natural products over a wide range of NMR and MS instruments. These experimentally collected data will allow us to pursue the remaining 3 computational objectives. These include: 1) developing fast, automated techniques to deconvolve NMR spectra from plants, foods and environmental samples; 2) using rule-based algorithms to improve the accuracy with which NMR spectra of natural products can be predicted to enable faster, more accurate compound identification and 3) creating rule-based methods to improve the speed and accuracy with which MS spectra of "modular" small molecules important in agri-bio and environmental science can be predicted, thereby improving their identification. This work will lead to new tools that should make metabolomics faster, cheaper and more comprehensive. It will allow metabolomics to be used in a wider number of disciplines, by far more users. It may also enable the migration of metabolomics into routine food analysis and environmental monitoring.