Synthetic Genetic Regulatory Networks to Study Emergent Properties of Development

该奖项由MCB的系统和合成生物学计划以及CBET的生物技术、生化和生物质工程计划资助，使用合成生物学来解决基因网络的特性。基因表达的调控在生物学的许多领域都是至关重要的，包括分化、干细胞生物学和组织的适当维护。为了确保适当的基因表达，多个基因在一个称为基因调控网络的复杂的相互作用网络中相互调节。理解基因调控网络功能的一种方法是尝试使用合成电路来构建该网络，然后直接研究合成网络的构建如何影响基因表达的稳定性和稳健性。在这个项目中，研究人员探索了这些稳定性和健壮性的概念，以及模式生物--黑腹果蝇(果蝇)的基因表达模式的敏锐性。为了在果蝇中建立合成电路，研究人员将首先开发新的合成生物学工具来控制复杂有机体的基因表达，这样就可以创建这些合成的基因调控网络。这项工作应该会为复杂生物体的合成生物学带来新的工具，以及对与果蝇模式形成相关的基因调控网络特性的新理解。研究人员还将与科学之家合作，科学之家是北卡罗来纳州立大学的一个组织，负责监督K-12的推广，让高中生和老师都能在生物学研究中使用工程学原理，目的是提高参与者S对在生物学研究中使用量化工具的欣赏和舒适度。技术说明：基因调控网络(GRN)，基因相互作用的复杂网络，被假设为解释发育中组织的新特性，如基因表达的健壮性和敏锐性。然而，这些特性是如何从特定的GRN基序中出现的假设很难验证，部分原因是在天然GRN中发现了高度的复杂性。为了克服这一困难，提出了一种替代方法，其中设计了更简单的合成GRN来直接测试这些假设。在这个项目中，研究人员将首先在果蝇中开发可调节的基因表达工具，包括使用核酶和其他RNA表达控制设备，以及用于设计此类表达控制设备的预测工具。然后，他们将研究由负反馈基序控制的基因表达的稳健性，以及受相互抑制基序控制的基因表达的敏锐性。以前对GRN涌现特性的研究已经从计算模型中产生了尚未经过检验的假说。这项工作将首先开发标准化工具来调节果蝇的基因表达水平，预计将适用于其他多细胞模式生物。这项工作还将测试特定的GRN基序如何导致所需的基因表达边界的稳健性和敏锐性等紧急特性。这项工作具有广泛的适用性，因为它关注的是网络主题，而不是特定的系统或信号通路。

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.