Computational Metabolomics: From Spectra to Knowledge

Motivation

Metabolomics is an analytical approach which aims to comprehensively describe the small molecule composition of a sample. Analyses are typically produced via mass spectrometry (MS) and/or nuclear magnetic resonance (NMR) spectroscopy. Computational tools for data processing and interpretation are critical to realizing the full potential of metabolomics in biological and biomedical research, environmental monitoring, or industrial biotechnology.

In recent years, network-based and multi-omics approaches have attracted a great deal of attention. Knowing that small molecules, in both biological and non-biological systems, are transformed over time, networks can be drawn which visualize and model small molecule transformations or other relationships. These networks provide information that can guide assignments of chemical structures and their annotations, and inform on mechanistic processes driving small molecule transformation (metabolism) in the sample. As an example of these networks, small molecules are nodes, and edges may be catalysts (synthetic, environmental) or gene products. While generic computational methods and metrics exist to construct and navigate networks, the appropriate definition of nodes and edges for a particular application is non-trivial and critical. Specifically, the metabolomics field needs computational methods that specifically address peculiarities of metabolomics data (e.g., identification, annotations, etc.). This task necessitates a deep knowledge of metabolomics, and that of other omics datasets when multi-omics integration is taken into account.

Building upon previous meetings, this multidisciplinary Dagstuhl Seminar will focus on improving interpretation of metabolomics data through network and statistical analysis of metabolomics data in a wider biological or environmental context (e.g., incorporation of other data types). This five-day seminar aims to bring together mass spectrometrists, NMR spectroscopists, statisticians, epidemiologists, biologists, and computer scientists to find solutions to the major challenges still remaining in this highly dynamic and rapidly evolving field.