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CL IX Theoretical Biology


The unforeseen complexity of the genetic and genomic architecture of barrier phenotypes prompts for new approaches in the understanding and clinical translation. The necessity to organize complex modeling approaches based on existing multidimensional datasets, the hypothesis based generation of new functional data under specific conditions in vitro and in vivo in humans which in turn refine such models has become equally important to the mere computational analysis of large-scale biological data sets. While individual groups have successfully built expertise in project related bioinformatics with an emphasis on genetic epidemiology and genome analysis, the applicants have identified computational/theoretical biology with its subfields bioinformatics and systems biology as a single emerging field of need. The further development will not only maximize competitiveness of the Cluster but also strengthen the clinical research environment at both host universities across different disciplines. A full professorship for Bioinformatics as well as the foundation of an Institute for Theoretical Biology between both universities has been decided to be established from core support (with advertisements for the Bioinformatics chair already ongoing). The specifications are on research with multidimensional life science data, preferably with a background in large-scale sequence data handling, genomics analysis and/or network modeling. Cluster support will add project embedded bioinformatics group leaders at both host universities and RCB as well as two associate professorships for systems biology with tenure track option. Complex datasets reflecting broad scale genetics and genomics explorations in cardiovascular disease and inflammatory bowel disease already exist. Besides research, a master with focus of bioinformatics and systems biology will be established.

Contribution to the Scientific Discourse

This CL will provide enabling tools and skills for the handling and analysis of multidimensional, large scale data sets derived from humans or relevant model organisms. It will (1) maintain and extend an IT environment that can handle such datasets intertwined with clinical data (see Data Management Concept, section RA Z), (2) provide a discussion and teaching environment to pre and postdocs for data banking, analysis and modeling tools, (3) establish and provide a collaborative systems biology modeling in key thematic areas and (4) contribute to undergraduate teaching of bioinformatics and system biology expertise including an specific master’s training.

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