Systems biology of cellular signaling : quantitative experimentation and systems genetics approaches

Cellular regulation is governed by dense biomolecular networks consisting of proteins, nucleotides, lipids, and metabolites that dynamically coordinate cellular decision making in the face of complex and time-varying environmental stimuli. Obtaining predictive models of these complex networks is a central goal of systems biology and requires sophisticated technologies for the acquisition and integration of many disparate data types. Recent genomic, proteomic and cellular imaging developments have greatly enabled systems-level studies, but further technological advances are needed. For instance, current high-throughput biochemical and cellular measurement techniques are generally limited to the analysis of cell populations, and the development of single-cell technologies are needed to advance predictive models of cellular networks. Large-scale genetic analyses are highly informative of the complex architecture of cellular networks but further computational methods are required to manage data complexity. In this thesis I present the development of two technologies, a microfluidic single-cell experimental platform and a genetic-network computational analysis platform, to address these issues and apply them to the study of prototypical eukaryotic signaling systems in Saccharomyces cerevisiae. First I describe microfluidic technology for the high-throughput analysis of single-cells subject to complex environmental conditions. Using this platform, I studied cellular response of the mating pathway in Saccharomyces cerevisiae under a series of genetic and time-varying environmental perturbations. This analysis revealed dynamic phenotypes that are not observable under static conditions and allowed for the stratification of system components into distinct functional roles. In addition, I describe advances to this technology that allow for the tracking of individual cells over long experimental time frames. These developments enabled the investigation of sources of cell-to-cell variability not detectable otherwise. Second I describe a computational platform for analyzing complex genetic interaction networks. These networks describe functional relationships between gene systems and can be used to delineate information flows through complex cellular circuits. Genetic interactions networks are dense and information rich, and require sophisticated computational methods for their analysis. In this work, I developed network algorithms to identify biologically informative patterns within a multi-mode genetic interaction network to reveal functional sub-networks and information-hubs of the filamentation pathway in Saccharomyces cerevisiae.Medicine, Faculty ofMedical Genetics, Department ofGraduat