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Our lab aims to stablish two research directions:

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2. The study of fundamental biological processes (e.g., health and disease) using metabolomics in combination with other omic platforms.

We are developing conceptual and computational new tools for de novo identification and characterization of unknown metabolites in metabolomics.

With the recent explosion of metabolomic research based on MS, today hundreds or thousands of metabolites can be measured simultaneously at a large scale from any given biological sample. Global metabolomic experiments indicate that the number of endogenous metabolites in biological systems is larger than anticipated and cannot be accounted for merely with canonical biochemical pathways. The challenge, though, is that most of these putative unanticipated (and unknown) metabolites are extremely difficult to characterize by the fact that both chemical structures of metabolites and annotated tandem MS spectra are largely unknown or incomplete in databases.

We are developing an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) data sets for global metabolomic experiments. We aim to develop integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and implement advanced statistical, chemometric, multivariate and artificial intelligence algorithms turning large measurement datasets into useful clinical information.

From a clinical and biomedical point of view, our main objective is to establish metabolomics as a tool for clinical diagnostics, elucidation of new therapeutic targets and unknown mechanisms associated with disease, especially diabetes, cardiovascular disease and cancer, which account for approximately two-thirds of all deaths in Europe and United States.