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: 1807520
• 负责人: Eduardo Sontag
• 金额: $ 35.44万
• 依托单位: Northeastern University
• 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 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807520&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，这在自然界中材料和动物身体结构的复杂模式中显而易见。 细胞通过使用编码在 DNA 中的相互作用的生物分子网络来构建这些结构，这些生物分子的功能就像微型计算机，随着许多细胞进行通信以共同解决问题，其能力也会增强。该项目的目标是通过设计显着提高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，就像人们对计算机进行编程一样，然后将其编译为 DNA 序列。新的理论工具将用于确定单元运行这些程序所需的功率，以及如何最好地在单元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，生物技术越来越多地用于商业生产从消费品到高端先进材料的各种产品。 当前的产品没有利用细胞的计算潜力；换句话说，所有基因始终处于开启状态。 这项研究将使细胞能够被编程为通过在细胞内部执行计算以及跨细胞通信来分多个步骤构建化学品和材料。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独立领域之间接受培训的学生的需求。 这包括新的本科水平合成生物学设计课程、工业合作社以及课程材料“如何种植几乎任何东西”，这些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遗传电路。问题在于，此类电路中最大的仅由约 10 个调节器组成，远小于自然网络，这极大地限制了可以执行的计算。拟议的研究将开发能够将计算复杂性大规模扩展至约 10^5 调节器的技术。第一个目标旨在增加细胞内电路的尺寸。 基于 Cas9 的逻辑门具有巨大的放大潜力，但受到 dCas9 毒性和序列重复的限制。将在数学模型的指导下设计一组门来解决这些问题。 将开发一个设计自动化框架，使 Verilog 规范能够转换为逻辑图，然后将其划分为许多交互单元。第二个目标旨在将遗传电路设计分布到多个通信细胞上。细胞间通信信号的数量和可靠性将通过定向进化来提高，将通道数量从2个增加到8个。这些将在活细胞和非生命系统中实现，从而实现在细胞内外的广泛应用生物反应器。结合 50 个门/单元，该平台提供了包含 10^5+ 门的多单元电路的可能性。某些应用程序需要部署为非生命系统，例如当应用程序位于实验室之外时，因此需要遏制。第三个目标旨在将目标 1 和 2 中开发的部分转化为在封装无细胞蛋白质提取物的多个连通脂质囊泡中运行。 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设计的自动化。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。