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Robustness, evolutionary optimality and plasticity of microbial signaling

Final Report Summary - MICROBE (Robustness, evolutionary optimality and plasticity of microbial signaling)

Being able to understand behavior and responses of organisms to their environment from perspective of evolutionary optimization is one of the major challenges for modern biology. In this respect, microorganisms offer multiple advantages as model systems due to their comparatively simple regulatory networks, well-defined behaviors and long evolutionary age. The main goal of this project was to perform a quantitative study of optimality, robustness and evolutionary plasticity of the cell signaling networks in microorganisms. As models for these studies, we used the well-tractable and relatively simple signaling networks in chemotaxis and environmental responses of the gut bacterium Escherichia coli and in the mating pathway of budding yeast Saccharomyces cerevisiae. To achieve this, a range of experimental techniques to quantitatively investigate bacterial and yeast behavior and to study intracellular function of cellular networks we established and applied. This quantitative experimental characterization of microbial networks was combined with their mathematical analysis and with computational modeling of evolutionary selection in microbial populations. The major achievement of our work was to demonstrate that the details of the network topology, integration of different signals or suppression of signaling noise within the pathway could be quantitatively explained by assuming that these networks have evolved to optimally and robustly function in a variable environment. Importantly, we showed that many network features can be explained in the context of population rather single-cell behavior. Furtehrmore, by performing experimental evolution of bacterial motility we showed how a complex network can optimize its function. This analysis suggested that evolutionary plasticity might itself be a selected property that explains certain features of the network. The experimental and modeling approaches and concepts that were established in the course of the project will be subsequently applied in studies of other microbial systems in our and in other groups.