Advertisement

Magnetic Nanoparticles-Loaded Physarum polycephalum: Directed Growth and Particles Distribution

  • Alice DimonteEmail author
  • Angelica Cifarelli
  • Tatiana Berzina
  • Valentina Chiesi
  • Patrizia Ferro
  • Tullo Besagni
  • Franca Albertini
  • Andrew Adamatzky
  • Victor Erokhin
Original Research Article

Abstract

Slime mold Physarum polycephalum is a single cell visible by an unaided eye. The slime mold optimizes its network of protoplasmic tubes to minimize expose to repellents and maximize expose to attractants and to make efficient transportation of nutrients. These properties of P. polycephalum, together with simplicity of its handling and culturing, make it a priceless substrate for designing novel sensing, computing and actuating architectures in living amorphous biological substrate. We demonstrate that, by loading Physarum with magnetic particles and positioning it in a magnetic field, we can, in principle, impose analog control procedures to precisely route active growing zones of slime mold and shape topology of its protoplasmic networks.

Keywords

Unconventional computing Physarum polycephalum Network Magnetic particles Analog control 

Notes

Acknowledgments

We acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the Collaborative project PhyChip, Grant Agreement No. 316366. We also acknowledge Yury Gunaza for the preparation of the artwork for the paper and Francesca Licci and Francesca Casoli for helpful discussions.

References

  1. 1.
    Nakagaki T, Yamada H, Toth A (2000) Maze-solving by an amoeboid organism. Nature 407:470CrossRefGoogle Scholar
  2. 2.
    Tsuda S, Aono M, Gunji YP (2004) Robust and emergent Physarum logical-computing. BioSystems 73:45–55CrossRefGoogle Scholar
  3. 3.
    Adamatzky A (2010) Physarum machines: computers from Slime Mould. World Scientific, SingaporeCrossRefGoogle Scholar
  4. 4.
    Adamatzky A, Jones J (2010) On electrical correlates of Physarum polycephalum spatial activity: can we see Physarum machine in the dark? Biophys Rev Lett 6:29–57CrossRefGoogle Scholar
  5. 5.
    Adamatzky A (2008) Physarum machine: implementation of a Kolmogorov–Uspensky machine on a biological substrate. Parallel Process Lett 19:105–127CrossRefGoogle Scholar
  6. 6.
    Adamatzky A (2009) Slime mould logical gates: exploring ballistic approach. Slime mould logical gates. arxiv:1005.2301v1 [nlin.PS]
  7. 7.
    Schumann A, Adamatzky A (2011) Physarum spatial logic. New Math Nat Comput 7:483–498CrossRefGoogle Scholar
  8. 8.
    Adamatzky A (2013) Slime mould tactile sensor. Sens Actuators B 188:38–44. doi: 10.1016/j.snb.2013.06.050 CrossRefGoogle Scholar
  9. 9.
    De Lacy Costello B, Adamatzky A (2013) Assessing the chemotaxis behavior of Physarum polycephalum to a range of simple volatile organic chemicals. Commun Integr Biol 6:25030CrossRefGoogle Scholar
  10. 10.
    Gale E, Adamatzky A, De Lacy Costello B (2013) Are slime moulds living memristors? Are slime moulds living memristors? arxiv:1306.3414 [cs.ET]
  11. 11.
    Adamatzky A, De Lacy Costello B, Shirakawa T (2008) Universal computation with limited resources: Belousov–Zhabotinsky and Physarum computers. Int J Bifurc Chaos 18:2373–2389CrossRefGoogle Scholar
  12. 12.
    Adamatzky A (2009) If BZ medium did spanning trees these would be the same trees as Physarum built. Phys Lett A 373:952–956CrossRefGoogle Scholar
  13. 13.
    Licci F, Rinaldi S, Besagni T (1986) Method for the preparation of fine hexagonal ferrite powders, in particular for magnetic recording. U.S. patent no. 4 622, p 159Google Scholar
  14. 14.
    Bolzoni F, Cabassi R (2004) Review of singular point detection techniques. Phys B Phys Condens Matter 346:524–527CrossRefGoogle Scholar
  15. 15.
    Adamatzky A (2010) Manipulating substances with Physarum polycephalum. Mater Sci Eng C 30:1211–1220CrossRefGoogle Scholar
  16. 16.
    Dussutour A, Latty T, Beekman M, Simpson SJ (2010) Amoeboid organism solves complex nutritional challenges. PNAS 107:1–5CrossRefGoogle Scholar
  17. 17.
    Adamatzky A (2009) Steering plasmodium with light: dynamical programming of Physarum machine. Steering plasmodium with light: dynamical programming of Physarum machine arxiv:0908.0850 [nlin.PS]
  18. 18.
    Shirakawa T, Konagano R, Inoue K (2012) Novel taxis of the Physarum plasmodium and a taxis-based simulation of Physarum swarm. In: SCIS-ISIS, Kobe, Japan, November 20–24Google Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Alice Dimonte
    • 1
    Email author
  • Angelica Cifarelli
    • 1
    • 2
  • Tatiana Berzina
    • 1
  • Valentina Chiesi
    • 1
  • Patrizia Ferro
    • 1
  • Tullo Besagni
    • 1
  • Franca Albertini
    • 1
  • Andrew Adamatzky
    • 3
  • Victor Erokhin
    • 1
  1. 1.National Council of the Researches – Institute of Materials for Electronics and Magnetism (CNR-IMEM)ParmaItaly
  2. 2.Department of Physics and Earth ScienceUniversity of ParmaParmaItaly
  3. 3.Unconventional Computing CentreUniversity of the West of EnglandBristolUK

Personalised recommendations