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Prediction and Control in DNA Nanotechnology.

Authors
  • DeLuca, Marcello1
  • Sensale, Sebastian2
  • Lin, Po-An1
  • Arya, Gaurav1
  • 1 Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States. , (United States)
  • 2 Department of Physics, Cleveland State University, Cleveland, Ohio 44115, United States. , (United States)
Type
Published Article
Journal
ACS applied bio materials
Publication Date
Feb 19, 2024
Volume
7
Issue
2
Pages
626–645
Identifiers
DOI: 10.1021/acsabm.2c01045
PMID: 36880799
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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.

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