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: 1146926
• 负责人: Ilias Tagkopoulos
• 金额: $ 27.11万
• 依托单位: University of California-Davis
• 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 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1146926&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的领域，其应用范围从生物制造到替代能源。尽管意义重大，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到更复杂的合成系统，超越少数相互作用的部分，一个可扩展的，综合的方法是必要的。与集成电路类似，在电路工程中，详细的计算机模型、优化方法、模拟器和设计工具的作用至关重要。智力优势：本项目旨在为基于优化的自动化合成基因电路设计框架铺平道路，该框架遵循用户定义的约束。合成基因回路是一个或多个基因的集合，连同元件（启动子、核糖体结合位点等）。影响基因表达。合成基因回路中的布线，即每个元件的顺序和位置，决定了基因表达模式和回路的整体行为。 这些回路通常作为质粒的一部分引入宿主生物体中，可以容易地操纵宿主生物体以实现期望的结果（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速度和实现更稳健、更可预测的设计，PI将开发一个设计和优化工具原型。为此，PI提出了一种新的优化配方，包括与合成基因电路设计相关的多个生物模型。此外，他们还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其他影响。调查将集中在如何从组合优化的最先进的技术可以应用于找到一个特定的任务的最佳电路。由于该工具需要一个充分表征的组件库才能运行，PI将创建三个广泛使用的调节器的突变库，然后对其进行定量表征，并将此信息存储在公开的数据库中。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，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：一个基于优化的合成生物学设计工具有可能为学术界提供服务，大大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。所有的组件和特征库，将开发作为这个奖项的一部分将是公开的，存放在合成生物学社区？的标准零件注册表。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，该团队是一个合成生物学本科团队，参加年度IGEM比赛。该项目的知识将通过ECS 289 K“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“生物信息学算法”（加州大学戴维斯分校）课程直接转移到课堂上。Arizona）。