The key goal of the biocenter is to achieve top biomedical research in an interdisciplinary setting. Our department of bioinformatics is already by its name and by the composition of its members highly interdisciplinary (scientists with medical and biological training, computer science, mathematics, as well as with a background in chemistry, pharmacology) and as we require for new insights large amounts of data (genomics, transcriptomics, proteomics, metabolomics). Hence we collaborate with most groups of the biocenter as well as with the faculty of biology (including ecology where there are systems and species data), and numerous chairs in medical faculty. Further collaborations are also with computer sciences (databanks, e-learning, development of algorithms), chemistry (drug design), mathematics (differential equations), physicists, (systems biology).
As is usual with a well set-up department, we have also numerous national and international collaborations for instance with LMU Munich, University of Heidelberg, University of Freiburg, University of Jena, with institutes such as ISAS Dortmund, MPI Berlin and foreign universities including Oxford and Harvard. The dept. of bioinformatics integrates a number of strong and complementary research groups. A unifying interest is a better understanding of cellular molecular networks, central also for Prof. Dandekaŕs research. We have a long-standing experience to analyze RNA and protein molecules by different bioinformatical methods, so that we are in a position to get an overview on the functions of basic components in a cellular network. This includes tools to study RNA functions such as the RNA analyzer and Riboswitch finder and a joined effort in the department studies RNA phylogeny (ITS2 database), for protein analysis the AnDOM server as well as sophisticated sequence and domain analysis techniques. At the same time we developed a number of algorithms and techniques to study the large-scale behaviour of such networks. This includes tools to model and calculate metabolic networks, for instance YANA, YANAsquare, YANAvergence as well as for regulatory networks and functional genomics (InGeno, GENOVA, JANE). We developed also cellular simulations as well as algorithms to identify reliable subnetworks in complex data assemblies (e.g. gene expression data and proteomics data).