Veit Schwämmle is Associate Professor for Computational Proteomics at the Department of Biochemistry and Molecular Biology since 2018. He received his PhD degree in Physics at the University of Stuttgart in 2006, followed by post-doctoral fellowships and an employment as assistant professor at the Centro de Pesquisas Fisicas in Rio de Janeiro, ETH Zürich and University of Southern Denmark. He received funding for his research from various agencies including DAAD, the Danish Research Council and ELIXIR Denmark.
Details about current and former research projects, access to software and associations to international initiatives can be found here.
Before changing to the field of Proteomics in 2008, he created and applied computer models to simulate sand dunes, biological evolution and linguistic phenomena, and worked on a generalization of statistical mechanics using generalized entropies and Fokker-Planck equations. At the University of Southern Denmark, Veit developed software to improve the analysis of proteomics data and implemented the first quantitative measure for the crosstalk between post-translational modifications.
He is co-founder of EuBIC and plays and active role in ELIXIR projects.
Head of research: Associate professor Veit Schwämmle
The Computational Proteomics Group develops and applies computational solutions for improved data analysis in large-scale omics experiments with focus on proteins and their post-translational modifications (PTMs). The aim is to better understand the functional protein states in order to determine, confirm and predict their contribution to cell behavior and disease.
Our main research interests are
- Software development for data from protein mass spectrometry experiments
- Chromatin biology: regulatory control by histone modifictions
- Tools for quantification and interpretation of omics data
- Simulations of molecular pathways
Automated workflow composition in mass spectrometry-based proteomics
Palmblad, M.; Lamprecht, A-l.; Ison, J.; Schwämmle, V., Bioinformatics, 2019 Feb 15;35 (4); 656–664
VSClust: feature-based variance-sensitive clustering of omics data
Schwämmle, V.; Jensen, O N., Bioinformatics, 2018 Sep 01; 34 (17); 2965–2972
Solitary wave behaviour of sand dunes
Schwämmle, V.; Herrmann, H J., Nature, 2003, 426,619–620