Skip to main content
SpringerLink
Log in

Computational Ability of Cells based on Cell Dynamics and Adaptability

  • Tutorial on Programming Natural Systems: Part 3. Programming Cellular Systems
  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

Learning how biological systems solve problems could help to design new methods of computation. Information processing in simple cellular organisms is interesting, as they have survived for almost 1 billion years using a simple system of information processing. Here we discuss a well-studied model system: the large amoeboid Physarum plasmodium. This amoeba can find approximate solutions for combinatorial optimization problems, such as solving a maze or a shortest network problem. In this report, we describe problem solving by the amoeba, and the computational methods that can be extracted from biological behaviors. The algorithm designed based on Physarum is both simple and useful.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Nakagaki, T., Yamada, H. and Tóth, A., “Maze-solving by an amoeboid organism,” Nature 407, pp. 470, 2000.

  2. Nakagaki, T., Yamada, H. and Tóth, A., “Path finding by tube morphogenesis in an amoeboid organism,” Biophys. Chem. 92, pp. 47–52, 2001

    Article  Google Scholar 

  3. Aono, M., and Gunji, Y-P., “Beyond input-output computings: Error-driven emergence with parallel non-distributed slime mold computer,” BioSystems, 71, pp. 257–287, 2003

    Article  Google Scholar 

  4. Tsuda, S., Zauner, K. P. and Gunji, Y-P., “Robot Control with Biological Cells,” in Proc. IPCAT, pp. 202–216, 2005.

  5. Aono, M. and Hara, M., “Dynamic Transition among Memories on Neurocomputer Composed of Amoeboid Cell with Optical Feedback,” in Proc.NOLTA 2006, Bologna, Italy, pp. 763–766, 2006.

  6. Aono, M. and Hara, M., “Amoeba-based Nonequilibrium Neurocomputer Utilizing Fluctuations and Instability,” in UC 2007, LNCS 4618(Aki, S.G., et al. eds.), pp. 41–54, Springer-Verlag, Berlin, 2007.

  7. Aono, M., Hara, M. and Aihara, K., “Amoeba-based Neurocomputing with Chaotic Dynamics,” Comm. ACM, 50(9), pp. 69–72, 2007

    Article  Google Scholar 

  8. Adamatzky, A., “Physarum machine: implementation of a Kolmogorov-Uspensky machine on a biological substrate,” Parallel Processing Letters, 2007.

  9. Shirakawa, T. and Gunji, Y. -P., “Emergence of morphological order in the network formation of Physarum polycephalum,” Biophys. Chem., 128, pp. 253-260, 2007

    Article  Google Scholar 

  10. Aono, M. and Hara, M., “Spontaneous deadlock breaking on amoeba-based neurocomputer,” BioSystems, 91, pp. 83–93, 2008

    Article  Google Scholar 

  11. Special issue on Physarum Computing in Int. J. Unconventional Computing, 2008.

  12. Kamiya, N., “Protoplasmic streaming”, in Protoplasmatologia, (L.V. Heilbrunn ed.), 8, Springer, 1959.

  13. Kessler, D., “Plasmodial structure and motility,” in Cell biology of Physarum and Didymium (Aldrich, H.C. and Daniel, J.W., eds.), pp. 145–196, Academic Press, New York, 1982.

  14. Nakagaki, T., “Smart behavior of true slime mold in labyrinth,” Res. Microbiol. 152, pp. 767–770, 2001

    Article  Google Scholar 

  15. Narby, J., Intelligence In Nature -An Inquiry Into Knowledge-, pp. 95–121, Penguin Group Inc., New York, 2005

    Google Scholar 

  16. Steinbock, O., Tóth, Á., and Showalter, K., “Navigating complex labyrinths: Optimal paths from chemical waves,” Science 267, pp. 868–871, 1995

    Article  Google Scholar 

  17. Nakagaki, T., Yamada, H. and Ueda, T., “Interaction between cell shape and contraction pattern,” Biophys. Chem. 84, pp. 195–204, 2000

    Article  Google Scholar 

  18. Nakagaki, T. and Guy, R., “Intelligent behaviors of amoeboid movement based on complex dynamics of soft matter,” Soft Matter, 4, pp. 1–2, 2008

    Article  Google Scholar 

  19. Tero, A., Kobayashi, R. and Nakagaki, T., “Mathematical model for adaptive transport network in path finding by true slime mold,” J. Theor. Biol. 244,pp. 553–564, 2007

    Article  MathSciNet  Google Scholar 

  20. Miyaji T., Ohnishi I., “Mathematical analysis to an adaptive network of the Plasmodium system,” Hokkaido Mathematical Journal, 36, pp. 445–465, 2007

    MathSciNet  Google Scholar 

  21. Miyaji, T., Ohnishi, I., “Physarum can solve the shortest path decision problem mathematically rigorously,” International Journal of Pure and Applied Mathematics, in press.

  22. Miyaji, T., Ohnishi, I., Tero, A., and Nakagaki, T., “Failure to the shortest path decision of an adaptive transport network with double edges in Plasmodium system,” Int. J. of Dynamical Systems and Differential Equations,” in press.

  23. Tero, A., Kobayashi, R. and Nakagaki, T., “Physarum solver -A biologically inspired method for road-network navigation-,” Physica A363, pp. 115, 2006.

  24. Nakagaki, T., Iima, M., Ueda, T., Nishiura, Y., Saigusa, T., Tero, A., Kobayashi, R., and Showalter, K., “Minimum-risk path finding by an adaptive amoebal network,” Phys. Rev. Lett., 99, 068104.

  25. Nakagaki, T., Yamada, H. and Hara, M., “Smart network solutions in an amoeboid organism,” Biophys. Chem. 107, pp. 1–5, 2004

    Article  Google Scholar 

  26. Nakagaki, T., Kobayashi, R., Nishiura, Y. and Ueda, T., “Obtaining multiple separate food sources: Behavioural intelligence in the Physarum plasmodium,” in Proc. R. Soc. Lond. B 271, pp. 2305–2310, 2004

    Article  Google Scholar 

  27. Tero, A., Yumiki, K., Kobayashi, R., Saigusa, T., Nakagaki, T., “Flow-network adaptation in Physarum amoebae,” Theory in Biosciences, 127, pp. 89–94, 2008

    Article  Google Scholar 

  28. Tero, A., Nakagaki, T., Toyabe, K., Yumiki, K. and Kobayashi, R., “A method inspired by Physarum for solving the Steiner problem,”Int. Journ. of Unconventional Computing, in press, (2008).

  29. Nakagaki, T., Saigusa, T., Tero, A., and Kobayashi, R., “Effects of food amount on path selection in transport network of an amoeboid organism,” Topological Aspects of Critical Systems and Networks, pp. 94–100, 2007.

  30. Akiyama, J. and Graham, R. L., Introduction to Discrete Mathematics, pp. 86–103, Asakura Shoten, Tokyo, ISBN4-254-11419-2, in Japanese, 1993.

  31. Dijkstra, W., Numer. Math. 1, pp. 269, 1959.

  32. Ouchi, A., Yamamoto, M., Kawamura, H., Shiba, T., Takayanagi, T., Toma, N., and Endo, S, Paradigm of computing from living complex systems, Morikita Shuppan, Tokyo ISBN4-627-8502102, in Japanese, 2003.

  33. Dorigo, M. and Stutzle, T., Ant Colony Optimization, The MIT Press, Massachusetts, USA, 2004.

    MATH  Google Scholar 

  34. Adamatzky, A., De Lacy Costello, B. and Asai, T., Reaction-diffusion computers, Elsevier, 2005.

Download references

Author information

Authors and Affiliations

  1. Research Institute for Electronic Science, Hokkaido University, Sapporo, 060-0812, Japan

    Toshiyuki Nakagaki

  2. PRESTO JST, 4-1-8 Honcho Kawaguchi, Saitama, Japan

    Atsushi Tero

  3. Department of Mathematics and Life Sciences, Japan Hiroshima University, Higashi-Hiroshima, 739-8626, Japan

    Ryo Kobayashi, Isamu Onishi & Tomoyuki Miyaji

Authors
  1. Toshiyuki Nakagaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atsushi Tero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryo Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isamu Onishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomoyuki Miyaji
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Tutorial series of three invited papers

About this article

Cite this article

Nakagaki, T., Tero, A., Kobayashi, R. et al. Computational Ability of Cells based on Cell Dynamics and Adaptability. New Gener. Comput. 27, 57–81 (2008). https://doi.org/10.1007/s00354-008-0054-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-008-0054-8

Keywords

Search

Navigation