Advertisement

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.

Keywords

Logic Gate Training Sequence Training Instance Substrate Molecule Computational Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations