Towards a Biomolecular Learning Machine

  • Matthew R. Lakin
  • Amanda Minnich
  • Terran Lane
  • Darko Stefanovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7445)

Abstract

Learning and generalisation are fundamental behavioural traits of intelligent life. We present a synthetic biochemical circuit which can exhibit non-trivial learning and generalisation behaviours, which is a first step towards demonstrating that these behaviours may be realised at the molecular level. The aim of our system is to learn positive real-valued weights for a real-valued linear function of positive inputs. Mathematically, this can be viewed as solving a non-negative least-squares regression problem. Our design is based on deoxyribozymes, which are catalytic DNA strands. We present simulation results which demonstrate that the system can converge towards a desired set of weights after a number of training instances are provided.

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References

  1. 1.
    Lawson, C.L., Hanson, B.J.: Solving least squares problems. Prentice-Hall, Englewood Cliffs (1974)MATHGoogle Scholar
  2. 2.
    Lederman, H., Macdonald, J., Stefanovic, D., Stojanovic, M.N.: Deoxyribozyme-based three-input logic gates and construction of a molecular full adder. Biochemistry 45(4), 1194–1199 (2006)CrossRefGoogle Scholar
  3. 3.
    Macdonald, J., Li, Y., Sutovic, M., Lederman, H., Pendri, K., Lu, W., Andrews, B.L., Stefanovic, D., Stojanovic, M.N.: Medium scale integration of molecular logic gates in an automaton. Nano Letters 6(11), 2598–2603 (2006)CrossRefGoogle Scholar
  4. 4.
    Minsky, M., Papert, S.: Perceptrons: an introduction to computational geometry, 2nd edn. MIT Press, Cambridge (1972)Google Scholar
  5. 5.
    Pei, R., Matamoros, E., Liu, M., Stefanovic, D., Stojanovic, M.N.: Training a molecular automaton to play a game. Nature Nanotechnology 5, 773–777 (2010)CrossRefGoogle Scholar
  6. 6.
    Qian, L., Winfree, E.: Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011)CrossRefGoogle Scholar
  7. 7.
    Qian, L., Winfree, E., Bruck, J.: Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011)CrossRefGoogle Scholar
  8. 8.
    Santoro, S.W., Joyce, G.F.: A general-purpose RNA-cleaving DNA enzyme. PNAS 94, 4262–4266 (1997)CrossRefGoogle Scholar
  9. 9.
    Stojanovic, M.N., Mitchell, T.E., Stefanovic, D.: Deoxyribozyme-based logic gates. JACS 124, 3555–3561 (2002)CrossRefGoogle Scholar
  10. 10.
    Zhang, D.Y., Seelig, G.: DNA-Based Fixed Gain Amplifiers and Linear Classifier Circuits. In: Sakakibara, Y., Mi, Y. (eds.) DNA16 2010. LNCS, vol. 6518, pp. 176–186. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Zhang, D.Y., Seelig, G.: Dynamic DNA nanotechnology using strand-displacement reactions. Nature Chemistry 3, 103–113 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthew R. Lakin
    • 1
  • Amanda Minnich
    • 1
  • Terran Lane
    • 1
  • Darko Stefanovic
    • 1
  1. 1.Department of Computer ScienceUniversity of New MexicoAlbuquerqueUSA

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