CPS: Frontier: Collaborative Research: BioCPS for Engineering Living Cells

CPS：前沿：合作研究：用于工程活细胞的 BioCPS

• 批准号: 1446607
• 负责人: Calin Belta
• 金额: $ 188.29万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446607&HistoricalAwards=false
• 关键词: CPS; Frontier; Collaborative; Research; BioCPS

Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem. This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells. The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.

纳米技术和合成生物学的最新发展使生物工程的一个新方向：在多细胞细菌和哺乳动物系统中的集体行为和时空模式的合成。这将在无定形计算、纳米制造、特别是组织工程等领域产生巨大影响，其中模式可用于将干细胞分化为组织和器官。虽然最近的技术，如组织和类器官芯片有可能在组织工程和药物开发中产生范式转变，但在细胞群中合成用户指定的新兴行为是释放这种潜力的关键一步，仍然是一个具有挑战性的未解决的问题。该项目将合成生物学和微米级移动的机器人技术结合在一起，以定义下一代网络物理系统（CPS）的基础，称为生物CPS（bioCPS）。用于决策和细胞间局部通信的合成基因电路使用生物设计自动化（BDA）工作流程自动合成。作为该项目的一部分，设计和建造了一个用于蜂窝网络（RA）通信，传感和控制的机器人助手，用于在工程细胞网络中生成所需的模式。在RA中，工程细胞与一组微型机器人相互作用，这些机器人实施控制，传感和远程通信策略，以实现所需的全局行为。微型机器人包括生物和非生物物质（附着在无机基底上的工程细胞，可以使用外部施加的场进行控制）。该技术用于测试活细胞中各种图案的形成。该项目有一个内容丰富的教育和推广计划，其中包括在全国范围内开展高中生CPS教育活动、高中生和本科生的实验室图尔斯参观和竞赛、讲习班、研讨会和研究生课程，以及针对代表性不足群体的具体举措。该项目的核心是理论和计算工具的开发，这将大大推进CPS的最新发展。提出了一种新的，正式的方法方法，在当地相互作用的代理大集合的紧急，全球性的行为合成。特别是，开发了一种新的逻辑，其公式可以有效地从分割图像的四叉树表示中学习。逻辑的定量语义将本地控制和通信协议的合成映射到优化问题。该项目通过开发一种机器学习方法来从大量数据中学习时态逻辑分类器，从而为时态逻辑推理的新生领域做出了贡献。新的抽象和验证技术的随机动力系统的定义和使用的BDA工作流程中的基因电路的正确性进行验证。