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.

Creative Commons BY 4.0

Corey Broeckling, Timothy Ebbels, Ewy Mathé, and Nicola Zamboni

• Sebastian Böcker (Universität Jena, DE) [dblp]
• Corey Broeckling (Colorado State University - Fort Collins, US)
• Roman Bushuiev (The Czech Academy of Sciences - Prague, CZ)
• Timothy M. D. Ebbels (Imperial College London, GB)
• Soha Hassoun (Tufts University - Medford, US)
• Carolin Huber (UFZ - Leipzig, DE)
• Katerina Kechris (University of Colorado - Aurora, US)
• Oliver Kohlbacher (Universität Tübingen, DE) [dblp]
• Anneli Kruve (Stockholm University, SE)
• Tytus Mak (NIST - Gaithersburg, US)
• Lennart Martens (Ghent University, BE) [dblp]
• Ewy A. Mathé (National Institutes of Health - Bethesda, US) [dblp]
• María Eugenia Monge (CIBION - Buenos Aires, AR)
• Steffen Neumann (IPB - Halle, DE) [dblp]
• Louis-Felix Nothias (University of Geneva, CH)
• Rui Pinto (Imperial College London & UK-Dementia Research Institute - London)
• Tomas Pluskal (The Czech Academy of Sciences - Prague, CZ) [dblp]
• Hannes Röst (University of Toronto, CA) [dblp]
• Juho Rousu (Aalto University, FI) [dblp]
• Francesco Russo (SSI - Copenhagen, DK)
• Adriano Rutz (University of Geneva, CH)
• Michael Andrej Stravs (Eawag - Dübendorf, CH)
• Panteleimon Takis (Imperial College London, GB)
• Justin J. J. van der Hooft (Wageningen University, NL) [dblp]
• Cecilia Wieder (Imperial College London, GB)
• Egon Willighagen (Maastricht University, NL)
• Michael Anton Witting (Helmholtz Zentrum München, DE)
• Mitja Zdouc (Wageningen University, NL)