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

协作：EAGER：基于模型的系统，用于通过数学优化自动设计合成遗传电路

• 批准号: 1147844
• 负责人: John Kececioglu
• 金额: $ 2.89万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147844&HistoricalAwards=false
• 关键词: Collaborative; EAGER; Model; Based; System

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).

合成生物学是一个新兴领域，应用范围从生物制造到替代能源。尽管生物电路工程意义重大，但它仍然依赖于反复尝试的修补技术，计算支持有限。如果合成生物学要发展到更复杂的合成系统，超越少数相互作用的部分，一种可扩展的、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高无上的。智能优点：该项目旨在为遵循用户定义的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以及元件(启动子、核糖体结合位点等)的集合。影响基因表达的基因。合成基因电路中的连接，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。这些回路通常作为质粒(S)的一部分被引入宿主生物体中，可以很容易地进行操纵，以达到预期的结果(例如特定的时间行为，或酶的产生)。为了促进更快的上市时间解决方案和更强大、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公式，该公式包含了与合成遗传电路设计相关的多个生物模型。此外，他们提出了一种混合优化-模拟技术来捕捉与细胞分裂、噪声和进化过程相关的额外影响。研究的重点将是如何应用组合优化的最新技术来为特定任务找到最优电路。由于该工具需要一个表征良好的组件库才能运行，PI将创建三个广泛使用的调节器的变异库，然后对它们进行定量表征，并将这些信息存储在公开可用的数据库中。作为概念验证实验，他们将通过构建自动设计的合成电路，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，来评估他们的综合方法。更广泛的影响：一种用于合成生物学的基于优化的设计工具有可能通过大幅缩短合成设计的上市时间而为学术界提供服务，并提供对生物功能的洞察，从而加速在一个指数级增长的领域的研究。将作为该奖项的一部分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-S标准件注册中心。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IGEM年度比赛的合成生物学本科生团队。该项目的知识将通过课程ECS 289k“系统和合成生物学中的计算挑战”(加州大学戴维斯分校)和课程CSC 450/550“生物信息学的算法”(加州大学亚利桑那州分校)直接转移到课堂上。