Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States.
Department of Physics, Cleveland State University, Cleveland, Ohio 44115, United States.
ACS Appl Bio Mater. 2024 Feb 19;7(2):626-645. doi: 10.1021/acsabm.2c01045. Epub 2023 Mar 7.
DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.
DNA 纳米技术是一个快速发展的领域，它使用 DNA 作为纳米级结构的构建材料。该领域发展的关键是能够使用模拟和其他建模技术准确描述 DNA 纳米结构的行为。在这篇综述中，我们介绍了 DNA 纳米技术中预测和控制的各个方面，包括各种分子模拟尺度、统计力学、动力学建模、连续介质力学和其他预测方法。我们还讨论了人工智能和机器学习在 DNA 纳米技术中的当前应用。我们讨论了如何协同结合实验和建模来提供对器件行为的控制，使科学家能够有信心地设计分子结构和动态器件，以确保它们按预期运行。最后，我们确定了 DNA 纳米技术缺乏足够预测能力的过程和场景，并提出了这些薄弱领域的可能解决方案。
