Discovery of general principles underlying signal transduction networks is essential for understanding the extraordinary complexity of the genetic and biochemical networks emerging in all the fields of biomedical research. This proposal deals with one of potential """"""""design principles"""""""", the issue of robustness in biochemical networks. We use chemotaxis of E. coli as a model system for experimental and theoretical study of robustness. This extensively characterized protein network is responsible for detection and response of the bacteria to chemical gradients. Although some details are still missing, it is now quite clear how the signal, initiated by the binding of ligands to chemotaxis receptors, is transmitted to the flagellar motors via the phosphorylated form of a response regulator, and how the methylation of the receptors provides a feedback system responsible for adaptation. The hypothesis which will be checked experimentally is that some of the chemotaxis properties, such as the precision of adaptation, are robust with respect to significant variations of the biochemical parameters, e.g. of the enzyme concentrations. Other properties, such as the steady-state network activity or the adaptation time should be non-robust and should change with varying parameters. In fact, stochastic variations in enzyme concentrations inside genetically identical individuals should lead to the previously observed variability from cell to cell in these non-robust properties. Both these aspects: robustness and non-genetic individuality will be assessed in a series of quantitative experiments, which will be compared with predictions of theoretical models, developed in parallel. In addition to measuring the extent of robustness in this simple signal transduction networks, the project plans to advance several theoretical and experimental methods that would be useful for the study of other systems. For instance, fluorescence correlation spectroscopy techniques applied to single cells may become fundamental for future in vivo biochemistry. Analytical and simulation techniques developed here should also be applicable for general biological system analysis.
