Supervised Learning in an Adaptive DNA Strand Displacement Circuit

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9211)


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.



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.


  1. 1.
    Morens, D.M., Fauci, A.S.: Emerging infectious diseases: threats to human health and global stability. PLOS Pathog. 9(7), e1003467 (2013)CrossRefGoogle Scholar
  2. 2.
    Zhang, D.Y., Seelig, G.: Dynamic DNA nanotechnology using strand-displacement reactions. Nat. Chem. 3(2), 103–113 (2011)CrossRefGoogle Scholar
  3. 3.
    Qian, L., Winfree, E.: A simple DNA gate motif for synthesizing large-scale circuits. J. R. Soc. Interface 8(62), 1281–1297 (2011)CrossRefGoogle Scholar
  4. 4.
    Qian, L., Winfree, E., Bruck, J.: Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Banda, P., Teuscher, C., Lakin, M.R.: Online learning in a chemical perceptron. Artif. Life 19(2), 195–219 (2013)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 9.
    Cardelli, L.: Strand algebras for DNA computing. Nat. Comput. 10(1), 407–428 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lakin, M.R., Youssef, S., Cardelli, L., Phillips, A.: Abstractions for DNA circuit design. J. R. Soc. Interface 9(68), 470–486 (2012)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Qian, L., Winfree, E.: Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011)CrossRefGoogle Scholar
  14. 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) CrossRefGoogle Scholar
  15. 15.
    Oishi, K., Klavins, E.: Biomolecular implementation of linear I/O systems. IET Syst. Biol. 5(4), 252–260 (2011)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 17.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)Google Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    Minsky, M., Papert, S.: Perceptrons: an introduction to computational geometry, 2nd edn. MIT Press, Cambridge (1972) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of New MexicoAlbuquerqueUSA
  2. 2.Center for Biomedical EngineeringUniversity of New MexicoAlbuquerqueUSA

Personalised recommendations