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Supervised Learning in an Adaptive DNA Strand Displacement Circuit

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DNA Computing and Molecular Programming (DNA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9211))

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Abstract

The development of DNA circuits capable of adaptive behavior is a key goal in DNA computing, as such systems would have potential applications in long-term monitoring and control of biological and chemical systems. In this paper, we present a framework for adaptive DNA circuits using buffered strand displacement gates, and demonstrate that this framework can implement supervised learning of linear functions. This work highlights the potential of buffered strand displacement as a powerful architecture for implementing adaptive molecular systems.

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References

  1. Morens, D.M., Fauci, A.S.: Emerging infectious diseases: threats to human health and global stability. PLOS Pathog. 9(7), e1003467 (2013)

    Article  Google Scholar 

  2. Zhang, D.Y., Seelig, G.: Dynamic DNA nanotechnology using strand-displacement reactions. Nat. Chem. 3(2), 103–113 (2011)

    Article  Google Scholar 

  3. Qian, L., Winfree, E.: A simple DNA gate motif for synthesizing large-scale circuits. J. R. Soc. Interface 8(62), 1281–1297 (2011)

    Article  Google Scholar 

  4. Qian, L., Winfree, E., Bruck, J.: Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011)

    Article  Google Scholar 

  5. Lakin, M.R., Minnich, A., Lane, T., Stefanovic, D.: Design of a biochemical circuit motif for learning linear functions. J. R. Soc. Interface 11(101), 20140902 (2014)

    Article  Google Scholar 

  6. Banda, P., Teuscher, C., Lakin, M.R.: Online learning in a chemical perceptron. Artif. Life 19(2), 195–219 (2013)

    Article  Google Scholar 

  7. Banda, P., Teuscher, C., Stefanovic, D.: Training an asymmetric signal perceptron through reinforcement in an artificial chemistry. J. R. Soc. Interface 11, 20131100 (2014)

    Article  Google Scholar 

  8. Soloveichik, D., Seelig, G., Winfree, E.: DNA as a universal substrate for chemical kinetics. Proc. Natl. Acad. Sci. USA 107(12), 5393–5398 (2010)

    Article  Google Scholar 

  9. Cardelli, L.: Strand algebras for DNA computing. Nat. Comput. 10(1), 407–428 (2010)

    Article  MathSciNet  Google Scholar 

  10. Lakin, M.R., Youssef, S., Cardelli, L., Phillips, A.: Abstractions for DNA circuit design. J. R. Soc. Interface 9(68), 470–486 (2012)

    Article  Google Scholar 

  11. Genot, A.J., Zhang, D.Y., Bath, J., Turberfield, A.J.: Remote toehold: a mechanism for flexible control of DNA hybridization kinetics. J. Am. Chem. Soc. 133, 2177–2182 (2011)

    Article  Google Scholar 

  12. Zhang, D.Y., Turberfield, A.J., Yurke, B., Winfree, E.: Engineering entropy-driven reactions and networks catalyzed by DNA. Science 318, 1121–1125 (2007)

    Article  Google Scholar 

  13. Qian, L., Winfree, E.: Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011)

    Article  Google Scholar 

  14. Zhang, D.Y., Seelig, G.: DNA-based fixed gain amplifiers and linear classifier circuits. In: Sakakibara, Y., Mi, Y. (eds.) DNA 16 2010. LNCS, vol. 6518, pp. 176–186. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Oishi, K., Klavins, E.: Biomolecular implementation of linear I/O systems. IET Syst. Biol. 5(4), 252–260 (2011)

    Article  Google Scholar 

  16. Yordanov, B., Kim, J., Petersen, R.L., Shudy, A., Kulkarni, V.V., Phillips, A.: Computational design of nucleic acid feedback control circuits. ACS Synth. Biol. 3(8), 600–616 (2014)

    Article  Google Scholar 

  17. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    Google Scholar 

  18. Lakin, M.R., Youssef, S., Polo, F., Emmott, S., Phillips, A.: Visual DSD: a design and analysis tool for DNA strand displacement systems. Bioinformatics 27(22), 3211–3213 (2011)

    Article  Google Scholar 

  19. Minsky, M., Papert, S.: Perceptrons: an introduction to computational geometry, 2nd edn. MIT Press, Cambridge (1972)

    Google Scholar 

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Acknowledgments

This material is based upon work supported by the National Science Foundation under grants 1318833 and 1422840. M.R.L. gratefully acknowledges support from the New Mexico Cancer Nanoscience and Microsystems Training Center.

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Authors and Affiliations

  1. Department of Computer Science, University of New Mexico, Albuquerque, NM, 87131, USA

    Matthew R. Lakin & Darko Stefanovic

  2. Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, 87131, USA

    Matthew R. Lakin & Darko Stefanovic

Authors
  1. Matthew R. Lakin
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  2. Darko Stefanovic
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Corresponding author

Correspondence to Matthew R. Lakin .

Editor information

Editors and Affiliations

  1. Microsoft Research, Cambridge, United Kingdom

    Andrew Phillips

  2. Wyss Institute, Boston, Massachusetts, USA

    Peng Yin

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Lakin, M.R., Stefanovic, D. (2015). Supervised Learning in an Adaptive DNA Strand Displacement Circuit. In: Phillips, A., Yin, P. (eds) DNA Computing and Molecular Programming. DNA 2015. Lecture Notes in Computer Science(), vol 9211. Springer, Cham. https://doi.org/10.1007/978-3-319-21999-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-21999-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21998-1

  • Online ISBN: 978-3-319-21999-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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