Most signaling and regulatory functions in the cell are executed by protein networks rather than by individual proteins, and it is therefore essential for biology to understand how the design and function of these networks have been shaped by the evolution. Despite differences in biological functions and molecular composition, all protein networks underlie similar evolutionary constrains on their performance and design, such as necessity to be robust against extra- and intracellular perturbations or to extract information from noisy environment. Moreover, although it can be assumed that a particular network has evolved to optimally solve certain problem, detailed quantitative analyses of optimality of the network performance are largely missing. Similarly little explored is the question of the network plasticity: how networks adapt to changing environmental conditions by adjustments of their structure and function, either regulating protein levels or undergoing microevolutionary changes. The goal of this proposal is to elucidate general features responsible for robustness and plasticity of cellular networks, using signaling networks in bacteria and yeast as well-tractable and relatively simple model systems. To achieve that, we will combine quantitative real-time analyses of the network function, primarily using high-throughput fluorescence microscopy, with computational modeling and with experimental microevolution, while exposing the networks to such common intra- and extracellular perturbations as variations in protein levels and in temperature. We expect our comparative analysis to provide general insights into the mechanisms of robustness and evolutionary optimization of cellular networks, thereby substantially advancing our understanding of biological systems.