SemiSynBio: Collaborative Research: Very Large-Scale Genetic Circuit Design Automation

SemiSynBio：合作研究：超大规模遗传电路设计自动化

• 批准号: 1807461
• 负责人: Kate Adamala
• 金额: $ 100万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807461&HistoricalAwards=false
• 关键词: SemiSynBio; Collaborative; Research; Very; Large

The computing power of biology is incredible, evident in the natural world in the intricate patterns underlying materials and the body plan of animals. Cells build these structures by using networks of interacting bio-molecules, encoded in their DNA, that function as microscopic computers, the power of which grows as many cells communicate to work together on a problem. The goal of this project is to significantly scale-up the ability to build these systems by design such that cells can be programmed to perform complex computational tasks. This will be done by creating software that allows a user to write code, exactly as one would program a computer, which is then compiled to a DNA sequence. New theoretical tools will be applied to determine the power required by the cell to run these programs and how best to distribute tasks between circuits encoded in cells and conventional electronic systems. This research will broadly impact biotechnology, which is increasingly being used to commercially produce a wide range of products, from consumer goods to high-end advanced materials. Current products do not harness the computational potential of cells; in other words, all the genes are turned on all the time. This research will enable cells to be programmed to build chemicals and materials in multiple steps, both by performing the computations inside of the cells and also communicating across cells. This work is interdisciplinary and requires backgrounds in Biology, Chemistry, Mathematics, Biological Engineering, Electrical Engineering, and Computer Science. As such, the project includes the development of new educational platforms in anticipation of a need in industry for students trained at the interface between traditionally separated fields. This includes a new undergraduate-level Synthetic Biology Design course, an industrial co-op, and curriculum material "How to Grow Almost Anything," which will be made public at an international level. To build the complexity of the natural world, cells use regulatory networks made up of interacting bio-molecules to control the timing and conditions for gene regulation. For the last 20 years, researchers have been able to build synthetic genetic circuits by artfully combining regulatory interactions. The problem is that the largest of such circuits only consist of ~10 regulators, far smaller than natural networks, which drastically limits the computation that can be performed. The proposed research will develop technologies that collectively enable a massive scale-up in computational complexity to ~10^5 regulators. The first objective seeks to increase the size of circuits within cells. Logic gates based on Cas9 have enormous scale-up potential, but are limited by dCas9 toxicity and sequence repeats. A set of gates will be designed to fix these problems, guided by mathematical modeling. A framework for design automation will be developed that enables a Verilog specification to be converted into a logic diagram, that is then divided up amongst many interacting cells. The second objective seeks to distribute a genetic circuit design across multiple communicating cells. The number and reliability of cell-cell communication signals will be improved by directed evolution to increase the number of channels from 2 to 8. These will be implemented in living cells and non-living systems, thus enabling a broad range of applications inside and outside the bioreactor. Combined with 50 gates/cell, this platform offers the possibility of multicellular circuits containing 10^5+ gates. Some applications require deployment as a non-living system, for example when the application is outside of the lab, thus requiring containment. The third objective seeks to translate the parts developed in Objectives 1 and 2 to operate in multiple communicating lipid vesicles encapsulating cell-free protein extract. Cas9 gates and additional communication channels will be characterized to expand the computational potential. These will be characterized as gates and implemented using Electronic Design Automation tools to automate the design of large systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物学的计算能力令人难以置信，在自然世界中具有复杂的材料基础模式和动物的身体计划。 细胞通过使用编码在DNA中的相互作用的生物分子的网络来构建这些结构，该网络充当微观计算机，随着许多细胞的沟通以在问题上共同工作时，该结构的功能。该项目的目的是显着扩展通过设计构建这些系统的能力，以便可以对单元进行编程以执行复杂的计算任务。 这将通过创建允许用户编写代码的软件来完成，就像一个人会编程计算机一样，然后将其编译为DNA序列。将应用新的理论工具来确定单元运行这些程序所需的功率，以及如何最好地在单元格和常规电子系统中编码的电路之间分配任务。这项研究将广泛影响生物技术，从消费品到高端高级材料，越来越多地用于商业生产广泛的产品。 当前产品不利用细胞的计算潜力。换句话说，所有基因始终开启。 这项研究将使细胞能够通过执行细胞内部的计算以及跨细胞进行通信来编程以多个步骤构建化学物质和材料。这项工作是跨学科的，需要生物学，化学，数学，生物工程，电气工程和计算机科学领域的背景。因此，该项目包括开发新的教育平台，以期在行业中需要在传统分离的领域之间的界面培训的学生。 这包括一个新的本科生合成生物学设计课程，工业合作社和课程材料“如何成长几乎任何东西”，将在国际层面上公开。为了建立自然世界的复杂性，细胞使用由相互作用的生物分子组成的调节网络来控制基因调节的时间和条件。在过去的20年中，研究人员能够通过巧妙地结合法规相互作用来建立合成的遗传回路。问题在于，最大的电路仅由约10个调节器组成，比自然网络小得多，这极大地限制了可以执行的计算。拟议的研究将开发技术，使计算复杂性的大规模扩大到〜10^5的调节剂。第一个目标旨在增加细胞内电路的大小。 基于CAS9的逻辑门具有巨大的扩展潜力，但受DCAS9毒性和序列重复的限制。在数学建模的指导下，将设计一组门来解决这些问题。 将开发一个设计自动化的框架，使Verilog规范可以转换为逻辑图，然后将其划分为许多相互作用的单元格。第二个目标试图在多个通信细胞上分布遗传电路设计。细胞电池通信信号的数量和可靠性将通过指示进化将通道数量从2增加到8的数量提高。这些通道数将在活细胞和非生存系统中实现，从而实现了生物反应器内外的广泛应用。该平台与50个门/单元相结合，提供了包含10^5+门的多细胞电路。某些应用程序需要部署作为非生存系统，例如，当应用程序不在实验室外时，因此需要遏制。第三个目标旨在翻译目标1和2中开发的部分，以在包含无细胞蛋白质提取物的多种通信脂质囊泡中运行。 CAS9门和其他通信渠道将被表征以扩大计算潜力。这些将被描述为大门，并使用电子设计自动化工具实施，以自动化大型系统的设计。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。