6th round table

September 20, 2013

Amazing protein folds
Manfred Sippl, Department of Structural Biology and Bioinformatics, University of Salzburg

Exploring gene expression signatures to predict drug sensitivity in cancer
Andreas Bernthaler, Cancer Bioinformatics, Boehringer Ingelheim Regional Center Vienna GmbH & Co KG

 

 

Amazing protein folds
Manfred Sippl, Department of Structural Biology and Bioinformatics, University of Salzburg

With the massive increase in the number of solved protein structures we begin to see more clearly how new protein folds arise from old templates. In particular it has become obvious that proteins evolve as molecular complexes as opposed to single chain entities.

Investigation and exploration of the manifold phylogenetic and functional relations among protein structures and protein complexes require specific tools for fast retrieval and visualization of structural matches. The problems involved challenge some fundamental issues in bioinformatics. We discuss current developments and challenges.

  

Exploring gene expression signatures to predict drug sensitivity in cancer
Andreas Bernthaler, Cancer Bioinformatics, Boehringer Ingelheim Regional Center Vienna GmbH & Co KG

In recent years the genomic landscape of tumors has proven to be one of the most influential factors in cancerogenesis. Even though the genomic foundation of cancer has been evident for more than 40 years, chemotherapeutic treatment which is  mainly based on a ‘hit or miss’ principle have been used until very recently.

Advances in high-throughput technologies allow for whole cell-state measurement of gene expression, protein expression, detection of small and large genomic variants, and epigenetic modifications to name just a few. With this development in mind, finding strategies for more specific therapies and hypothesis driven selection of cancer models is an emerging desire in the field of oncology. Here we correlate gene expression data to the proliferative response of cancer cell lines using statistical approaches and machine learning methodologies. Subsequently, we show that in principle it is possible to predict drug response based on gene expression data.