Noise, Bistability & Beneficial Heterogeneity in the Galactose Signaling Network

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DESCRIPTION (provided by applicant): The galactose signaling network in the budding yeast Saccharomyces cerevisiae has been serving as the paradigm for gene regulation in eukaryotic cells for more than 50 years. During this period a wealth of genetic and biochemical data on this network was collected resulting in a nearly complete understanding of the molecular interactions between the genes and proteins in this network. However, despite extensive data on its molecular interactions, an a priori prediction of its dynamical system behavior is still very challenging. The main reason for this lies in the complex organization of multiple nested feedback loops in the GAL network, which makes an analysis of the system dynamics difficult. In this proposal the galactose signaling pathway of the budding yeast Saccharomyces cerevisiae is used as a model system to develop a detailed quantitative understanding of the dynamical system properties of a network containing multiple transcriptional feedback controls. Additionally we will explore what the consequences of these dynamical properties are regarding its physiological relevance and its impact on the fitness of a population. Since the GAL network has been studied extensively during the last five decades a detailed knowledge of its main components and interactions has been developed making this network an ideal candidate for a system-level analysis. The proposal focusses on three specific aims each reflecting an important concept of stochastic dynamical systems: noise, bistability, and beneficial heterogeneity. First, the stochastic switching dynamics of single yeast cells will be explored as a function of time. Additionally, the epigenetic inheritance of the switch properties will be studied both experimentally and theoretically. Second a new method will be used to deduce the stability conditions of a feedback controlled genetic switch by using open loop networks. Finally, the physiological relevance of stochastic gene expression will be addressed by exploring if a dynamically heterogeneous population can achieve a higher net growth rate than a homogenous population when exposed to fluctuating environments. The proposed work will not only lead to a detailed understanding of feedback regulation in the GAL network and its consequences for gene expression dynamics and population fitness, but will also provide novel experimental and theoretical techniques and concepts that will be important for the analysis of gene networks in higher eukaryotes.