"Quantitative, predictive models for cellular signaling network"

An increasing number of cellular signaling networks are well characterized in terms of components and interactions. Yet how functionality arises remains unclear. In close collaboration with experimental groups we develop detailed, quantitative models to unravel the mechanistic details and to arrive at a thorough understanding of biological signaling. Importantly, all our models have predictive power, and  predictions are tested in experiments. By example of two bacterial signaling networks that regulate cell fate decisions in B. subtilis, and a stress response in E.coli I will illustrate the approach. Finally I will comment on how the method can be used to address similar questions also in higher organisms where less detail is known.