Computational Systems Medicine

Research Group @ Chair of Experimental Bioinformatics @ TUM
Our group aims to elucidate the molecular mechanisms behind phenotypes in general and human diseases, in particular. To that end, we develop integrative bioinformatics methods leveraging network analysis, machine learning techniques, and statistical approaches. We apply own and existing approaches in close collaboration with biologists and physicians to derive insights from multi-omics data.

Olga Tsoy will join as a Post-Doc. Amit Fenn and Gihanna Galindez join as PhD Students. Chit Tong Lio starts her Master’s thesis. …

Tim Kacprowski has been invited to give a talk at the Jahressymposium der Interdisziplinären Gruppe für Labor und Durchflussyztometrie.

Tim Kacprowski has been invited to give a talk at the 9th Gene Quantification Event qPCR dPCR & NGS 2019.



The EU H2020 project REPO-TRIAL aims at developing an in silico approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrate heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome) and develop graph-based machine learning approaches to investigate this highly complex data.


The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.

Network-Based Epistasis Detection

We tackle the challenge of higher-order epistasis detection using biological networks to narrow the search space and GPU computing to improve the efficiency. Phenotype-specific epistasis-modules extracted from larger networks will help to better understand the underlying biological mechanisms of different phenotypes.

De novo network enrichment

We develop tools that leverage information from molecular interaction networks in understanding molecular profiling data. De novo network enrichment tools extract subnetworks that mechanistically explain a phenotype of interest, e.g. a disease.

Software & Resources

Coming soon