Cited by 295DNA and Biological ComputingAdvanced biosensing and bioanalysis techniquesModular Robots and Swarm Intelligence

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Diverse and robust molecular algorithms using reprogrammable DNA self-assembly

Abstract

Molecular biology provides an inspiring proof-of-principle that chemical systems can store and process information to direct molecular activities such as the fabrication of complex structures from molecular components. To develop information-based chemistry as a technology for programming matter to function in ways not seen in biological systems, it is necessary to understand how molecular interactions can encode and execute algorithms. The self-assembly of relatively simple units into complex products 1 is particularly well suited for such investigations. Theory that combines mathematical tiling and statistical-mechanical models of molecular crystallization has shown that algorithmic behaviour can be embedded within molecular self-assembly processes 2,3, and this has been experimentally demonstrated using DNA nanotechnology 4 with up to 22 tile types 5-11. However, many information technologies exhibit a complexity threshold-such as the minimum transistor count needed for a general-purpose computer-beyond which the power of a reprogrammable system increases qualitatively, and it has been unclear whether the biophysics of DNA self-assembly allows that threshold to be exceeded. Here we report the design and experimental validation of a DNA tile set that contains 355 single-stranded tiles and can, through simple tile selection, be reprogrammed to implement a wide variety of 6-bit algorithms. We use this set to construct 21 circuits that execute algorithms including copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating cellular automata, generating deterministic and randomized patterns, and counting to 63, with an overall per-tile error rate of less than 1 in 3,000. These findings suggest that molecular self-assembly could be a reliable algorithmic component within programmable chemical systems. The development of molecular machines that are reprogrammable-at a high level of abstraction and thus without requiring knowledge of the underlying physics-will establish a creative space in which molecular programmers can flourish.

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Metadata

Authors
Woods, D., Doty, D., Myhrvold, C., Hui, J., Zhou, F., Yin, P., & Winfree, E.
Affiliations
  • California Institute of Technology (US)
  • Harvard University (US)
Corresponding author
  • Woods, Damien
  • Doty, David
  • Winfree, Erik
Venue
Nature
Published
2019-03
Volume
567
Issue
7748
Pages
366-372
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W2924487690
Cited by
295
References
36
Citations by year
2026: 22 · 2025: 38 · 2024: 35 · 2023: 49 · 2022: 47 · 2021: 50 · 2020: 36 · 2019: 18
FWCI
12.50
Type
journal article
Language
en
Open access
closed
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
  • Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
Keywords
DNA self-assembly, DNA nanotechnology, algorithmic self-assembly, sorting algorithms, cellular automata simulation, leader election, counting, error rate
MeSH terms
Algorithms, DNA, Reproducibility of Results, Nanotechnology

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