Collaborative: EAGER: A Model Based System for the Automated Design of Synthetic Genetic Circuits by Mathematical Optimization

合成生物学是一个新兴领域，其应用范围从生物制造到替代能源。尽管其意义重大，但生物电路工程仍然依赖于试错修补技术，计算支持有限。如果合成生物学要发展更复杂的合成系统，超越少数相互作用的部分，一个可扩展的、综合的、方法论的方法是必要的。与集成电路类似，当涉及到电路工程时，详细的计算机模型，优化方法，模拟器和设计工具的作用是至关重要的。智力优势：该项目旨在为遵循用户定义约束的合成基因电路的基于优化的自动化设计框架铺平道路。合成基因回路是一个或多个基因的集合，以及影响基因表达的元件（启动子、核糖体结合位点等）。合成基因电路中的线路，即每个元件的顺序和位置，决定了基因表达模式和电路的整体行为。这些电路通常作为质粒的一部分被引入宿主生物中，可以很容易地操纵以达到预期的结果（例如特定的时间行为，或酶的产生）。为了加快解决方案的上市时间和实现更稳健、可预测的设计，pi将开发设计和优化工具原型。为此，pi提出了一种新的优化配方，其中包含与合成遗传电路设计相关的多种生物模型。此外，他们提出了一种混合优化模拟技术，以捕获与细胞分裂、噪声和进化过程相关的额外效应。调查将集中在如何从最先进的技术组合优化可以应用于找到一个特定任务的最佳电路。由于该工具将需要一个具有良好特征的组件库来操作，因此pi将创建一个包含三种广泛使用的调节器的突变库，然后对它们进行定量表征，并将此信息存储在一个公开可用的数据库中。作为一个概念验证实验，他们将通过构建一个自动设计的合成电路来评估他们的综合方法，测量其输出和与预期目标的偏差，然后将其与文献中已有的其他类似设计进行比较。更广泛的影响：基于优化的合成生物学设计工具有潜力为学术界提供服务，通过大幅减少合成设计的上市时间，并提供对生物功能的见解，从而加速指数增长领域的研究。作为该奖项的一部分，将开发的所有组件和特征库都将公开提供，并存放在合成生物学社区中。s标准零件注册表。此外，该奖项将部分支持加州大学戴维斯分校IGEM团队的工作和培训，这是一个参加年度IGEM比赛的合成生物学本科团队。该项目的知识将通过ECS 289K课程“系统和合成生物学中的计算挑战”（加州大学戴维斯分校）和CSC 450/550课程“生物信息学算法”（亚利桑那大学）直接转移到课堂上。

Synthetic Biology is a nascent field with applications that range from bio-fabrication to alternative energy. Despite its significance, engineering of biological circuits still relies on trial-and-error tinkering techniques, with limited computational support. If Synthetic Biology is to advance to more complex synthetic systems that go beyond a handful of interacting parts, a scalable, integrative, methodological approach is necessary. In an analogy to integrated circuits, when it comes to circuit engineering, the role of detailed computer models, optimization methods, simulators and design tools is paramount.Intellectual Merit: This project aims to pave the way towards an optimization-based, automated design framework for synthetic gene circuits that adhere to user-defined constraints. A synthetic gene circuit is a collection of one or more genes, together with elements (promoters, ribosome binding sites, etc.) that influence gene expression. The wiring, i.e. the order and position of every element, within a synthetic gene circuit determines the gene expression pattern, and overall behavior of the circuit. These circuits are introduced, usually as part of a plasmid(s), in a host organism that can be readily manipulated in order to achieve a desired outcome (e.g. specific temporal behavior, or production of an enzyme). To facilitate faster time-to-market solutions and more robust, predictable designs, PIs will develop a design and optimization tool prototype. To that end, PIs propose a new optimization formulation that encompasses multiple biological models relevant to synthetic genetic circuit design. In addition, they propose a hybrid optimization-simulation technique to capture additional effects related to cell division, noise, and evolutionary processes. The investigation will focus on how state-of-the-art techniques from combinatorial optimization can be applied to find the optimal circuit for a specific task. Since the tool will need a library of well-characterized components to operate, PIs will create a mutant library of three widely-used regulators, then quantitatively characterize them, and store this information in a publicly available database. As a proof-of-concept experiment, they will assess their integrative approach by constructing an automatically-designed synthetic circuit, measuring its output and deviation from the desired goal, and then comparing it to other similar designs that have been already available in literature. Broader Impact: An optimization-based, design tool for synthetic biology has the potential to provide a service to the academic community by reducing drastically the time-to-market aspect of synthetic designs, and providing insight on biological function, thus accelerating research in an exponentially growing field. All components and characterized libraries that will be developed as part of this award will be publicly available, deposited in the synthetic biology community?s standard Parts Registry. Furthermore, this award will partially support the work and training of the UC Davis IGEM team, a synthetic biology undergraduate team who competes in the annual IGEM competition. Knowledge from this project will be directly transferred into classrooms through the course ECS 289K "Computational Challenges in Systems and Synthetic Biology" (UC Davis), and the course CSC 450/550 "Algorithms for Bioinformatics" (U. Arizona).