Final Report Summary - SYNLET (Regulatory control networks of synthetic lethality)
This project addressed robustness of phenotypic function on the basis of highly interlinked network topologies on a very practical level: synthetic lethality - as proposed for novel cancer treatment regimes. Function in this context is the property of transformed cells to continuously divide, and robustness is encoded by regulatory elements on the genomic and proteomic level triggering drug treatment escape as an emergent property: chemotherapy resistance. This project derived novel concepts, methodologies, and their algorithmic implementation for annotation and analysis of general regulatory networks - with particular focus on network robustness and escape routes toward maintaining function. In parallel, computational genomics were applied on a gene expression data set derived from a unique, systematic collection of chemotherapy resistant cancer cell lines. These real world data encode a set of regulatory escape mechanisms to overcome the lethality imposed by a specific drug. The general concepts and approaches - focusing on dynamical levels - were subsequently calibrated and merged with results from computational genomics - focusing on a static system representation - to gain insight into the mechanisms of cancer cell robustness, but in particular to identify key proteins of cellular escape mechanisms to overcome lethality of drugs. To prove the validity of our complex systems analysis approach we tested generated hypotheses on synthetically lethal hubs in the cellular control network via experimental knock down experiments utilising siRNA.