BioXell, SpA
Institute for Research in Biomedicine
›› Ludwig Institute for Cancer Research, University College
Universidad Autonoma de Madrid
Vita-Salute San Raffaele University
Ludwig Institute for Cancer Research, University College

The University College London (UCL) branch of the Ludwig Institute for Cancer Research (LICR-UCL) is internationally renowned for its research into signal transduction in the context of human disease including cancer, inflammation and immune responses. Research groups at LICR-UCL work together in a highly integrated way to investigate the role of specific proteins and signaling networks in regulating cell behavior. The close interactions between groups provides an ideal environment for training of research students, since they have full access to the respective expertise of all the different groups, which range from bioinformatics to mouse genetics, Drosophila models and advanced microscopy techniques. Students also benefit from working with highly trained postdoctoral fellows from many different countries who have a wide variety of technical expertise and research backgrounds. At LICR-UCL the emphasis will be on training students in bioinformatics skills, which are highly sought after both in industry and academia throughout the EU. Increasingly, graduates with good training in bioinformatics are needed to interpret the large amounts of data generated by large-scale screens and proteomics. LICR-UCL is providing the bioinformatics core for the EC-funded network of excellence, MAIN. Within the MAIN framework, they have initiated a systems biology approach to studying signal transduction in endothelial cells, and groups within UCL that are using computational/systems biology methodologies have regular meetings. Students will benefit from exposure to the variety of computational approaches being used at LICR-UCL. In particular, they will be encouraged to carry out research projects where experiments are designed based on computational analysis of existing genomic, biochemical or proteomic data.