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.

生物学的计算能力是令人难以置信的，在自然世界中，在材料和动物的身体计划的复杂模式中显而易见。 细胞通过使用相互作用的生物分子网络来构建这些结构，这些生物分子编码在它们的DNA中，其功能就像微观计算机一样，随着许多细胞相互交流以共同解决一个问题，其能力就会增长。该项目的目标是通过设计来显着扩大构建这些系统的能力，以便可以对细胞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实现，就像对计算机编程一样，然后将其编译为DNA序列。新的理论工具将用于确定细胞运行这些程序所需的功率，以及如何最好地在细胞中编码的电路和传统电子系统之间分配任务。这项研究将广泛影响生物技术，生物技术正越来越多地用于商业生产各种产品，从消费品到高端先进材料。 目前的产品并没有利用细胞的计算潜力;换句话说，所有的基因都是一直开启的。 这项研究将使细胞能够通过多个步骤编程来构建化学物质和材料，既可以在细胞内进行计算，也可以在细胞间进行通信。这项工作是跨学科的，需要在生物学，化学，数学，生物工程，电气工程和计算机科学的背景。因此，该项目包括开发新的教育平台，以满足行业对在传统分离领域之间接受培训的学生的需求。 其中包括一门新的本科生水平合成生物学设计课程、一个工业合作社以及课程材料“如何种植几乎任何东西”，该材料将在国际层面上公开。为了构建自然世界的复杂性，细胞使用由相互作用的生物分子组成的调控网络来控制基因调控的时间和条件。在过去的20年里，研究人员已经能够通过巧妙地结合调控相互作用来构建合成基因电路。问题是，最大的此类电路仅由~10个调节器组成，远远小于自然网络，这极大地限制了可以执行的计算。拟议的研究将开发技术，共同使计算复杂性大规模扩大到约10^5个监管机构。第一个目标是增加细胞内电路的大小。 基于Cas9的逻辑门具有巨大的放大潜力，但受到dCas 9毒性和序列重复的限制。一组门将被设计来解决这些问题，由数学建模指导。 将开发一个设计自动化框架，使Verilog规范转换为逻辑图，然后在许多相互作用的单元之间划分。第二个目标是将基因电路设计分布在多个通信细胞上。通过定向演进将信道数量从2个增加到8个，将提高小区间通信信号的数量和可靠性。 这些将在活细胞和非生命系统中实施，从而实现生物反应器内外的广泛应用。结合50门/单元，该平台提供了包含10^5+门的多单元电路的可能性。一些应用需要作为非生命系统部署，例如当应用在实验室之外时，因此需要遏制。第三个目标是将目标1和2中开发的部分转化为在包封无细胞蛋白质提取物的多个连通脂质囊泡中操作。Cas9门和额外的通信通道将被表征以扩展计算潜力。这些将被描述为门，并使用电子设计自动化工具来实现大型系统的自动化设计。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。