Unraveling the Operational Landscape of Engineered Genetic Regulatory Networks

The operation of all living cells is executed by a collection of complex biomolecular regulatory networks. Naturally occurring networks exhibit remarkable properties as they robustly operate in an ever-changing cellular milieu. In this dissertation, we present theoretical and experimental results that investigate broadly, the cellular context in which regulatory networks operate and, more specifically, the nuanced regulatory action of a class of small non-coding RNA molecules called microRNAs. In investigating the cellular context, we commenced our work by exploring the impact that resource scarcity may have on regulatory network function by mathematically inferring gene interactions through the experimental perturbation of a synthetic gene circuit. We then investigate how the inherent stochastic environment of a cell affects protein production through the characterization of a panel of synthetic circuits stably integrated in human cells. We develop and validate a novel mathematical framework that can reliably attribute measured cellular noise to local and global fluctuation sources. In investigating microRNA function, we commenced our work by engineering custom genetic circuits that contain microRNA-based regulation and introduced an analytical strategy that utilizes the clustering and superposition of discrete experiments to produce a “bird’s-eye view” perspective capable of correlating the function of a microRNA to its concentration and the genetic circuit abundance. We then studied intragenic miRNA-mediated host transcript regulation, where the miRNA and its target are processed from the same gene transcript. To this end, a synthetic system was stably integrated in human kidney cells within a genomic cell safe harbor locus. We provide results showing robust output filtering with respect to promoter strength and a validation of the experimental observations based on stochastic population model simulations.