Modelling Bacterial metabolism: Smooth adaptation to different conditions

We study model organisms (Staphylococci, Salmonellae, Listeria) exploiting a number of large-scale data sets (genome, gene expression and proteome data, metabolite data, isotopologue labeling data). These reveal in detail the extensive and optimized adaptation potential of these organisms to changing conditions. There are a number of concerted pathway changes, key regulators such as PrfA in Listeria redirect metabolism. To cope with this strong bacterial adaptation potential in infections, new approaches include new anti-infectives. The study of the impact of these on metabolism is improved by the combination of tools and experimental data presented. Metabolism adapts also fast to xenobiotics, however, here a window of opportunity opens if the effect on human cells and their more limited metabolic adaptation potential is significantly less. Bioinformatically, new programs and scripts deal with the different data sets and allow prediction and tests of metabolic flux models as well as fit with low error to the different data sets. This furthermore allows to investigate on a more fundamental level how the different data-sets correlate and to identify points of strong enzymatic or mRNA regulation. We summarize the different studies in a comparison of the different life styles of intracellular pathogens regarding metabolism during infection.