The Computational Biology Group is part of the Department of Biosystems, Science and Engineering of ETH Zurich.

Quantitative predictive models of cellular signaling networks

We develop quantitative, predictive models of biological signaling networks with a view to gain a comprehensive understanding of the dynamics and evolution of cellular signaling.

Higher forms of life emerge from a more sophisticated use of often conserved signalling pathways to regulate biological function. The complex behaviour of the resulting cellular signalling networks is impossible to grasp by verbal models alone. Quantitative, computational models are required to integrate biological knowledge into a framework that permits the efficient generation of testable hypotheses and that enables an integrative understanding of biological networks. We develop mechanistic models for a range of biological problems.

All projects are carried out in close collaboration with experimental groups to ensure that quantitative data is available and that model predictions can be tested by experiments. Validated models are then used to further investigate the biological system and to address more general questions regarding the evolution and the design of cellular signaling networks. Our initial focus has been on bacterial systems because quantitative data can be more easily obtained. We increasingly consider eukaryotic systems where cellular networks are more complex. As a long term goal we strive to obtain predictive models for all important (human) cellular signaling networks. We are particularly interested in how signaling is integrated between different networks and how similar networks (and network components) can be used to respond in different contexts.