Synthetic Gene Circuits as Benchmarks for Understanding Biological Networks

The expression of genes is controlled by regulatory networks, which perform fundamental information processing and control mechanisms in a cell. Unraveling and modelling these networks will be indispensable to gain a systems-level understanding of biological organisms and genetically related diseases. With their ability to emulate and interface with naturally occurring networks, synthetic gene networks are powerful tools in this process. Recent advancements in genetic engineering technologies have expanded the possibilities in design and implementation of synthetic networks, offering unprecedented opportunities to examine and perturb their activity in cellular milieu. In this thesis, we present development and characterization of synthetic gene circuits constructed specifically for the purpose of mimicking and monitoring the regulatory strategies in human cells. First, we introduce a reverse engineering pipeline using synthetic gene circuit as a benchmark for biological reverse engineering. We discuss the advantages of this method and how one can engineer the circuits amenable to reverse engineering. Using several synthetic gene circuits, we show that network reconstruction results not only reproduce the benchmark network topologies, but also identifies a novel feature that can be critical towards solving a commonly misidentified topology. Furthermore, we consider the application of using synthetic circuits to characterize a rare and complex gene regulatory motif on its output expression. Specifically, we demonstrate that an intragenic miRNA-mediated output regulation operates as a filter with respect to promoter strength and reduces expression noise. Lastly, we present novel microRNA sensing systems based on CRISPR/Cas systems that offer new opportunities for engineering systems.