Prediction and Control in DNA Nanotechnology

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ACS Applied Bio Materials


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 thes


This work is supported by the National Science Foundation (Grant nos. CMMI-1921955 and EFMA-1933344) and the U.S. Department of Energy (Grant no. DE-SC0020996) . M.D. is supported by the National Science Foundation Graduate Research Fellowship (Grant no. DGE-2139754) . P.-A.L. is supported by the Duke University AI for Understanding and Designing Materials (aiM) Graduate Training Program funded by the National Science Foundation (Grant no. DGE- 2022040) .