AF:Small:Collaborative Research:Kinetics and Thermodynamics of Chemical Computation

AF：小：协作研究：化学计算的动力学和热力学

• 批准号: 1619343
• 负责人: David Doty
• 金额: $ 25万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619343&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Kinetics

Biology is replete with smart molecular systems that perform nanoscale assembly, sense environmental stimuli, create chemical signals, and produce physical motion - each of these tasks coordinated by information processing circuits implemented with chemical reactions. Learning how to build artificial chemicals that compute autonomously in complex environments would bring about groundbreaking advances in manufacturing, chemical sensing, and medicine. The theory of computation has proven invaluable in enabling information processing in electronic man-made systems, and much-studied algorithms underlie the behavior of everything from communication networks to video games. However, a thorough understanding of the principles of chemical computation is still lacking. The goal of this proposal is to use rigorous mathematical models to investigate the capabilities and limitations of chemical information processing.The proposed research will bring the fields of physics, chemistry, biology, and computer science closer intellectually and mutually enrich them. For example, conceptual frameworks and mathematical tools capturing the manipulation of information at the molecular level may yield critical insights into the design principles of evolved biological regulatory networks. Further, understanding how information processing is possible in the disordered world of chemistry could result in error-robust electronic computing. The project will also contribute to the development of undergraduate and graduate courses, which will train students to apply the principles of computer science and electrical engineering in traditionally incompatible domains. This will encourage the next generation of scientists to break through traditional disciplinary barriers and create the scientific and engineering fields of tomorrow. This proposal will answer foundational questions about the computational power of chemical kinetics (chemical reaction networks). How can chemicals be programmed to have desired behaviors? How much molecular energy does such computation consume? How much "more computation" does every additional chemical reaction enable? Recent advances in DNA nanotechnology (strand displacement cascades) demonstrate that molecular systems of complex functionality can be designed and constructed from the ground up. This proposal will help precisely delineate the capabilities and limitations of this technology, resulting in smaller, simpler DNA-based circuits. This proposal also introduces a new paradigm, based on the laws of thermodynamics, for programming DNA-DNA interactions. As chemical and biological systems are comprised of molecules that are inherently information-rich and programmable, principles of computer science will help design smart molecules.

生物学中充满了智能分子系统，这些系统执行纳米级组装、感知环境刺激、产生化学信号并产生物理运动——每一项任务都由通过化学反应实现的信息处理电路来协调。学习如何构建在复杂环境中自主计算的人造化学品将为制造、化学传感和医学带来突破性的进步。事实证明，计算理论在电子人造系统中实现信息处理方面具有无价的价值，并且经过大量研究的算法是从通信网络到视频游戏等一切行为的基础。然而，对化学计算原理的透彻理解仍然缺乏。该提案的目标是使用严格的数学模型来研究化学信息处理的能力和局限性。拟议的研究将使物理、化学、生物学和计算机科学领域在智力上更加紧密并相互丰富。例如，捕获分子水平信息操纵的概念框架和数学工具可能会对进化的生物调节网络的设计原理产生重要的见解。此外，了解如何在无序的化学世界中进行信息处理可能会导致错误鲁棒的电子计算。该项目还将促进本科生和研究生课程的开发，培训学生在传统上不兼容的领域应用计算机科学和电气工程原理。这将鼓励下一代科学家突破传统学科障碍，创造明天的科学和工程领域。该提案将回答有关化学动力学（化学反应网络）计算能力的基本问题。如何对化学品进行编程以使其具有所需的行为？这样的计算消耗多少分子能量？每个额外的化学反应可以实现多少“更多计算”？ DNA 纳米技术（链置换级联）的最新进展表明，可以从头开始设计和构建具有复杂功能的分子系统。该提案将有助于精确描述该技术的能力和局限性，从而产生更小、更简单的基于 DNA 的电路。该提案还引入了一种基于热力学定律的新范式，用于对 DNA-DNA 相互作用进行编程。由于化学和生物系统由本质上信息丰富且可编程的分子组成，计算机科学原理将有助于设计智能分子。

项目成果

Diverse and robust molecular algorithms using reprogrammable DNA self-assembly

使用可重编程 DNA 自组装的多样化且强大的分子算法

• DOI: 10.1038/s41586-019-1014-9
• 发表时间: 2019-03-01
• 期刊: Nature
• 影响因子: 64.8
• 作者: D. Woods;David Doty;C. Myhrvold;Joy Hui;Felix Y. Zhou;P. Yin;E. Winfree
• 通讯作者: E. Winfree