Construction of a Molecular Learning Network

Tomohiro Shirakawa; Hiroshi Sato

doi:10.20965/jaciii.2013.p0913

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JACIII Vol.17 No.6 pp. 913-918

doi: 10.20965/jaciii.2013.p0913

(2013)

Paper:

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Construction of a Molecular Learning Network

Tomohiro Shirakawa and Hiroshi Sato

Department of Computer Science, National Defense Academy of Japan, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan

Received:

May 2, 2013

Accepted:

September 26, 2013

Published:

November 20, 2013

Keywords:

associative learning, gene regulatory network, Physarum plasmodium

Abstract

Learning ability in unicellular organisms has been studied since the first half of the 20th century, but there is still no clear evidence of unicellular learning. Based on results from previous associative learning experiments using the Physarum plasmodium, a gene regulatory network model of unicellular learning was constructed. The model demonstrates that, in principle, unicellular learning can be achieved through the cooperation of several biomolecules.

Cite this article as:

T. Shirakawa and H. Sato, “Construction of a Molecular Learning Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.6, pp. 913-918, 2013.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J.W. French, “Trial and error learning in paramecium,” J. Exp. Psycol., Vol.26, pp. 609-613, 1940.

[2] [2] T. M. Hennessey, W. B. Rucker, and C. G. McDiarmid, “Classical conditioning in paramecia,” Anim. Learn Behav., Vol.7, pp. 417-423, 1979.

[3] [3] N. Gandhi, G. Ashkenasy, and E. Tannenbaum, “Associative learning in biochemical networks,” J. Theor. Biol., Vol.249, pp. 58-66, 2000.

[4] [4] C. T. Fernando, A. M. L. Liekens, L. E. H. Bingle, C. Beck, T. Lenser, D. J. Stekel, and J. E. Rowe, “Molecular circuits for associative learning in single-celled organisms,” J. R. Soc. Interface, Vol.6, pp. 463-469, 2008.

[5] [5] M. O. Magnasco, “Chemical kinetics is Turing universal,” Phys. Rev. Lett., Vol.78, pp. 1190-1193, 1997.

[6] [6] T. Nakagaki, H. Yamada, and Á. Tóth, “Maze-solving by an amoeboid organism,” Nature, Vol.407, p. 470, 2000.

[7] [7] T. Nakagaki, H. Yamada, and M. Hara, “Smart network solutions in an amoeboid organism,” Biophys. Chemist., Vol.107, pp. 1-5, 2003.

[8] [8] T. Shirakawa and Y.-P. Gunji, “Emergence of morphological order in the network formation of Physarum polycephalum,” Biophys. Chemist., Vol.128, pp. 253-260, 2007.

[9] [9] T. Saigusa, A. Tero, T. Nakagaki, and Y. Kuramoto, “Amoeba anticipate periodic events,” Phys. Rev. Lett., Vol.100, 018101, 2008.

[10] [10] T. Shirakawa, Y.-P. Gunji, and Y. Miyake, “An associative learning experiment using the plasmodium of Physarum plasmodium,” Nano Commun. Netw., Vol.2, pp. 99-105, 2011.

[11] [11] T. Lenser, T. Hinze, B. Ibrahim, and P. Dittrich, “Toward evolutionary network reconstruction tools for systems biology,” Proc. of the 5th European Conf. on Evolutionary Computation, machine learning and data mining in bioinformatics, pp. 132-142, April 2007.