The adaptation of living organisms to their environment is controlled at the molecular level by large and complex networks of genes, mRNAs, proteins, metabolites, and their mutual interactions. In order to understand the overall behavior of an organism, we must complement molecular biology with the dynamic analysis of cellular interaction networks, by constructing mathematical models derived from experimental data, and using simulation tools to predict the behavior of the system under a variety of conditions. Following this methodology, we have started the analysis of the network of global transcription regulators controlling the adaptation of the bacterium Escherichia coli to environmental stress conditions. Even though E. coli is one of the best studied organisms, it is currently little understood how a stress signal is sensed and propagated throughout the network of global regulators, so as to enable the cell to respond in an adequate way. Using a qualitative method that is able to overcome the current lack of quantitative data on kinetic parameters and molecular concentrations, we have modeled the carbon starvation response network and simulated the response of E. coli cells to carbon deprivation. This has allowed us to identify essential features of the transition between exponential and stationary phase and to make new predictions on the qualitative system behavior following a carbon upshift. The model predictions have been tested experimentally by means of gene reporter systems.