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Neural network computation with DNA strand displacement cascades

Author

Listed:
  • Lulu Qian

    (Bioengineering, California Institute of Technology)

  • Erik Winfree

    (Bioengineering, California Institute of Technology
    Computer Science, California Institute of Technology
    Computation and Neural Systems, California Institute of Technology)

  • Jehoshua Bruck

    (Computation and Neural Systems, California Institute of Technology
    Electrical Engineering, California Institute of Technology)

Abstract

A programmable DNA network Before neuron-based brains evolved, complex biomolecular circuits must have endowed individual cells with the intelligent behaviour that ensures survival. But the study of how molecules can 'think' has not yet produced useful molecule-based computational systems that mimic even a single neuron. In a study that straddles the fields of DNA nanotechnology, DNA computing and synthetic biology, Qian et al. use DNA as an engineering material to construct computing circuits that exhibit autonomous brain-like behaviour. The team uses a simple DNA gate architecture to create reaction cascades functioning as a 'Hopfield associative memory', which can be trained to 'remember' DNA patterns and recall the most similar one when presented with an incomplete pattern. The challenge now is to use the strategy to design autonomous chemical systems that can recognize patterns or molecular events, make decisions and respond to the environment.

Suggested Citation

  • Lulu Qian & Erik Winfree & Jehoshua Bruck, 2011. "Neural network computation with DNA strand displacement cascades," Nature, Nature, vol. 475(7356), pages 368-372, July.
  • Handle: RePEc:nat:nature:v:475:y:2011:i:7356:d:10.1038_nature10262
    DOI: 10.1038/nature10262
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    Citations

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    Cited by:

    1. Ferdinand Greiss & Nicolas Lardon & Leonie Schütz & Yoav Barak & Shirley S. Daube & Elmar Weinhold & Vincent Noireaux & Roy Bar-Ziv, 2024. "A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Ahmed A. Agiza & Kady Oakley & Jacob K. Rosenstein & Brenda M. Rubenstein & Eunsuk Kim & Marc Riedel & Sherief Reda, 2023. "Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Russell Bates & Oleg Blyuss & Ahmed Alsaedi & Alexey Zaikin, 2015. "Effect of Noise in Intelligent Cellular Decision Making," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    4. Karen Zhang & Yuan-Jyue Chen & Delaney Wilde & Kathryn Doroschak & Karin Strauss & Luis Ceze & Georg Seelig & Jeff Nivala, 2022. "A nanopore interface for higher bandwidth DNA computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Betz, Ulrich A.K. & Arora, Loukik & Assal, Reem A. & Azevedo, Hatylas & Baldwin, Jeremy & Becker, Michael S. & Bostock, Stefan & Cheng, Vinton & Egle, Tobias & Ferrari, Nicola & Schneider-Futschik, El, 2023. "Game changers in science and technology - now and beyond," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    6. Hong Kang & Yuexuan Yang & Bryan Wei, 2024. "Synthetic molecular switches driven by DNA-modifying enzymes," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Jianbang Wang & Zhenzhen Li & Itamar Willner, 2022. "Cascaded dissipative DNAzyme-driven layered networks guide transient replication of coded-strands as gene models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Avik Samanta & Maximilian Hörner & Wei Liu & Wilfried Weber & Andreas Walther, 2022. "Signal-processing and adaptive prototissue formation in metabolic DNA protocells," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    9. Kanakov, Oleg & Chen, Shangbin & Zaikin, Alexey, 2024. "Learning by selective plasmid loss for intracellular synthetic classifiers," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    10. Linlin Yang & Qian Tang & Mingzhi Zhang & Yuan Tian & Xiaoxing Chen & Rui Xu & Qian Ma & Pei Guo & Chao Zhang & Da Han, 2024. "A spatially localized DNA linear classifier for cancer diagnosis," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    11. Luna Rizik & Loai Danial & Mouna Habib & Ron Weiss & Ramez Daniel, 2022. "Synthetic neuromorphic computing in living cells," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    12. Colangeli, Matteo & Rugiano, Francesco & Pasero, Eros, 2014. "Pattern recognition at different scales: A statistical perspective," Chaos, Solitons & Fractals, Elsevier, vol. 64(C), pages 48-66.
    13. Tai-Yin Chiu & Hui-Ju K Chiang & Ruei-Yang Huang & Jie-Hong R Jiang & François Fages, 2015. "Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-27, September.

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