Systems Biology of Drug Perturbations
Combining and integrating different types of data (e.g., molecular in vitro data, data from cell-based assays, complex phenotypic data such as side effects), we aim at gaining a better understanding of drug mechanisms of action. Ideally, this will aid the interpretation of drug action in many contexts, from the molecular level (for instance, drug-target interaction) to the whole organism (e.g., establishing links between side effects and cellular pathways: Brouwers et al. PLoS One, 2011; see also Iskar et al. Curr. Opin. Biotechnol., 2012). To this end, we are developing methods to predict protein targets, response pathways or side effects of drugs from complex cell-based assays or organism-scale data and analyze these predictive models to reveal the underlying biological mechanisms. We see this as rational approaches to drug repositioning and in silico drug safety assessment.
Currently, we focus on cellular assays of expression changes upon chemical perturbations: The Connectivity Map records gene expression (for >10,000 genes) in several cell lines for hundreds of small molecules. Analysis of such complex multi-parametric read-outs requires intelligent supervised (e.g. feature selection) and unsupervised techniques (such as clustering and bi-clustering) in order to reveal new drug mechanisms of action as well as functions of the biological systems that respond to these treatments (see Iskar et al. Mol. Syst. Biol., 2013).
SIDER resource at EMBL | STITCH resource at EMBL | Drug modules at EMBL | CMap project at the BROAD