Synthetic Genetic Regulatory Networks to Study Emergent Properties of Development

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The award, funded by the Systems and Synthetic Biology Program in MCB and the Biotechnology, Biochemical and Biomass Engineering Program in the CBET, uses synthetic biology to address the properties of gene networks. Regulation of gene expression is crucial in many areas of biology, including differentiation, stem cell biology, and proper maintenance of tissues. To ensure proper gene expression, multiple genes regulate each other in a complex web of interactions called gene regulatory networks. One approach to understanding the function of a gene regulatory network is to try to construct that network using synthetic circuits, and then directly examine how the construction of the synthetic network impacts the stability and robustness of gene expression. In this project, investigators explore these concepts of stability and robustness, as well as sharpness in patterns of gene expression in the model organism, Drosophila melanogaster (the fruit fly). In order to create synthetic circuits in the fruit fly, the investigators will first develop new synthetic biology tools for controlling gene expression in a complex organism, such that these synthetic gene regulatory networks can be created. This work should lead to both new tools in synthetic biology for complex organisms as well as new understanding of gene regulatory network properties associated with pattern formation in the fruit fly. The investigators will also work with Science House, a North Carolina State University organization that oversees K-12 outreach to engage both high school students and teachers in the use of engineering principles in the study of biology, with a goal of enhancing participant?s appreciation and comfort with the use of quantitative tools in the study of biology.Technical description: Gene regulatory networks (GRNs), complex webs of genetic interactions, are hypothesized to explain emergent properties of developing tissues, such as the robustness and sharpness of gene expression. However, hypotheses of how these properties emerge from specific GRN motifs are difficult to verify, partly due to the high degree of complexity found in native GRNs. To overcome this difficulty, an alternative approach is proposed in which simpler, synthetic GRNs are designed to directly test these hypotheses. In this project, investigators will first develop tunable gene expression tools in Drosophila, including the use of ribozymes and other RNA expression control devices, as well as predictive tools for the design of such expression control devices. They will then investigate the robustness of gene expression when controlled by a negative feedback motif, as well as the sharpness of gene expression when controlled by a mutual repression motif. Previous studies of emergent properties of GRNs have produced as-yet untested hypotheses from computational models. This work will first develop standardized tools to tune gene expression levels in Drosophila, with expected applicability to other multicellular model organisms. The work will also test how specific GRN motifs result in the desired emergent properties of robustness and sharpness of gene expression boundaries. The work has broad applicability due to its focus on network motifs rather than specific systems or signaling pathways.