Research at the Department of Computational Biology of the Max-Planck-Institut für Informatik is focused on bioinformatics for better diagnosis and treatment of human diseases. Currently, twenty-five researchers develop computational methods that help uncover the etiology of important diseases (including cancer and infectious diseases) and they apply their results in close collaboration with pre-clinical and clinical researchers in order to improve diagnosis and treatment of patients.

Thomas Lengauer established the Department of Computational Biology at the Max-Planck-Institut für Informatik in 2001 and has been heading it since. He has been engaged in research in computational biology since the beginning of the 90s and is a founding member of the International Society for Computational Biology (ISCB), a member of the steering board of the international conference series RECOMB, and he headed the steering board of the European bioinformatics conference series ECCB since its foundation in 2002 until 2005.

Christoph Bock joined the Department of Computational Biology at the Max-Planck-Institut für Informatik in 2004 and established the department’s research initiative on computational epigenetics. As one of the first bioinformaticians in the field of epigenetics, he contributed to the increased recognition that epigenome-scale experiments require adequate computational tools for data analysis. He is the main developer of BiQ Analyzer, which has become a standard software for DNA methylation research, and EpiGRAPH, which facilitates the genome-scale analysis of epigenetic datasets.

In the context of the CANCERDIP proposal, the main tasks of the department of computational biology will be:

(i) Computational discovery and validation of hypotheses regarding functional interactions within the DNA methylation machinery (DNMTs, MBD proteins) and with a wide range of genomic and epigenomic features.

(ii) Computational ranking of all cancer-specific differentially methylation regions according to their predicted potential as cancer biomarkers.

(iii) Optimization and evaluation of the predictive power and robustness of selected biomarker candidates. In addition, statistical and bioinformatic support will be provided to all other work packages as required.