The group performs research in the exciting field of systems biology. By applying modern Bayesian inference techniques, we hope to improve software tools that life scientists use to analyse data in metabolomics — the study of small molecules produced by fundamental chemical processes in every living being. Led by Dr. Simon Rogers, researchers from the IDI group work on the two biggest bottlenecks of metabolomics data analysis today: performing accurate identification of metabolites, and assigning biological significance to the result.
The figure above shows various metabolites to identify (top), and the mass spectra from experimental data (bottom). Accurately identifying metabolites from experimental data remains a challenging problem that has not been fully addressed. Improvements in metabolomics data analysis would enhance our understanding of other aspects of systems biology, such as functional genomics, nutrition researches and disease biomarkers detections.
For more information, please contact the following researchers who are working on this subject:
- Mixture Model Clustering for Peak Filtering in Metabolomics. Simon Rogers, Rónán Daly and Rainer Breitling. Proceedings of the 9th International Workshop on Computational Systems Biology, WCSB 2012 to appear.
- Probabilistic assignment of formulas to mass peaks in metabolomics experiments S. Rogers, R.E. Scheltema, M. Girolami, R. Breitling Bioinformatics 25(4) 2009, 512–518[Available online] [journal website].
* Image by Markus Heinonen.