Max Planck Fellow

Oliver Kohlbacher

The research areas of the Kohlbacher Lab are rather diverse and span from classical bioinformatics topics including sequence, structure, and systems bioinformatics to translational bioinformatics and personalized medicine. 

Computational Immunomics

Computational immunomics applies bioinformatics methods to gain a deeper understanding of the immune system. Furthermore it assists medical research by providing computational models which help to solve immunology-related problems. Our group develops various computational immunomics methods, primarily for mass spectrometry (MHC ligandomics) and NGS (HLA typing, neoepitope discovery) based analyses.

Computational Mass Spectrometry and Metabolomics

We are focused on development and integration of analysis pipelines for molecular tumor table backends mainly centered around high-throughput data extraction and integration, automated processing of incoming data, annotation of therapeutic options and visualisation of network-derived contexts and analysis results.

Personalized Medicine

We are focused on development and integration of analysis pipelines for molecular tumor table backends mainly centered around high-throughput data extraction and integration, automated processing of incoming data, annotation of therapeutic options and visualisation of network-derived contexts and analysis results.

Structural Bioinformatics

Structural Bioinformatics is one of the long established research fields in our group and has strived various subfields comprising theoretical and applied computer-aided drug design (CADD), cheminformatics, molecular mechanics-based modelling, or the prediction of protein-protein complexes. Additionally, we spent a significant amount of our time in the development of high-quality software tools providing solutions to some of these challenges that we make publicly available. On this web page we briefly present selected topics of our current research.

Translational Bioinformatics

Translational Bioinformatics is a field at the interface of bioinformatics and medical informatics. By integrating molecular data (bioinformatics) and healthcare data (medical informatics), it becomes possible to identify identify new pathomechanisms, suggest personalized therapies, or enable machine learning on medical data. Current projects include the development of infrastructures and methods to enable molecular tumor boards and new methods for distributed, privacy-preserving data analytics.