Quantitative analysis of microbial networks

We are interested in quantitative understanding of the design and functioning of cellular networks. Although specific molecular mechanisms differ, any such system must be able to extract signals from a noisy environment, adapt to large changes in signal and background strengths, and integrate multiple cues. It can thus be expected that biological networks rely on a small number of common evolutionary design features. Elucidating these features requires a combination of quantitative analysis of the network in vivo with detailed computer modelling. Ultimately, we want to be able to explain why the observed network designs were evolutionary selected out of a large number of possibilities. As models, we use several relatively simple and well-studied networks in Escherichia coli. Most of our current work is done on the chemotaxis signal transduction pathway, although we recently started investigations of other networks in E. coli. We use fluorescence microscopy techniques to analyze network properties in vivo, including intracellular protein distributions, protein interactions, and spatio-temporal dynamics of the intracellular signal processing. These data are then combined with the available biochemical information to develop predictive computer models of the networks. We expect the evolutionary design principles that can be derived from such analyses to be general and equally apply them to more complex networks in eukaryotes. Signal processing in E. coli chemotaxis V. Aliaksiuk, M. Emmerich, D. Kentner, S. Neumann