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Improving Diffusion-Based Molecular Communication with Unanchored Enzymes

  • Adam NoelEmail author
  • Karen Cheung
  • Robert Schober
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 134)

Abstract

In this paper, we propose adding enzymes to the propagation environment of a diffusive molecular communication system as a strategy for mitigating intersymbol interference. The enzymes form reaction intermediates with information molecules and then degrade them so that they have a smaller chance of interfering with future transmissions. We present the reaction-diffusion dynamics of this proposed system and derive a lower bound expression for the expected number of molecules observed at the receiver. We justify a particle-based simulation framework, and present simulation results that show both the accuracy of our expression and the potential for enzymes to improve communication performance.

Keywords

Molecular communication Reaction-diffusion system Intersymbol interference Nanonetwork 

References

  1. 1.
    Alberts, B., Bray, D., Hopkin, K., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Essential Cell Biology, 3rd edn. Garland Science, New York (2010)Google Scholar
  2. 2.
    Akyildiz, I.F., Brunetti, F., Blazquez, C.: Nanonetworks: a new communication paradigm. Comput. Netw. 52(12), 2260–2279 (2008)CrossRefGoogle Scholar
  3. 3.
    Nakano, T., Moore, M.J., Wei, F., Vasilakos, A.V., Shuai, J.: Molecular communication and networking: opportunities and challenges. IEEE Trans. Nanobiosci. 11(2), 135–148 (2012)CrossRefGoogle Scholar
  4. 4.
    Nelson, P.: Biological Physics: Energy, Information, Life, 1st edn. W. H. Freeman and Company, New York (2008)Google Scholar
  5. 5.
    Hiyama, S., Moritani, Y.: Molecular communication: harnessing biochemical materials to engineer biomimetic communication systems. Nano Commun. Netw. 1(1), 20–30 (2010)CrossRefGoogle Scholar
  6. 6.
    Atakan, B., Akan, O.B.: Deterministic capacity of information flow in molecular nanonetworks. Nano Commun. Netw. 1(1), 31–42 (2010)CrossRefGoogle Scholar
  7. 7.
    Mahfuz, M.U., Makrakis, D., Mouftah, H.T.: Characterization of intersymbol interference in concentration-encoded unicast molecular communication. In: Proceedings of 2011 IEEE CCECE, pp. 164–168, May 2011Google Scholar
  8. 8.
    Einolghozati, A., Sardari, M., Beirami, A., Fekri, F.: Capacity of discrete molecular diffusion channels. In: Proceedings of 2011 IEEE ISIT, pp. 723–727, August 2011Google Scholar
  9. 9.
    Chou, C.T.: Molecular circuits for decoding frequency coded signals in nano-communication networks. Nano Comm. Netw. 3(1), 46–56 (2012)CrossRefGoogle Scholar
  10. 10.
    Nakano, T., Okaie, Y., Vasilakos, A.V.: Throughput and efficiency of molecular communication between nanomachines. In: Proceedings of 2012 IEEE WCNC, pp. 704–708, April 2012Google Scholar
  11. 11.
    Miorandi, D.: A stochastic model for molecular communications. Nano Commun. Netw. 2(4), 205–212 (2011)CrossRefGoogle Scholar
  12. 12.
    Moore, M.J., Suda, T., Oiwa, K.: Molecular communication: modeling noise effects on information rate. IEEE Trans. Nanobiosci. 8(2), 169–180 (2009)CrossRefGoogle Scholar
  13. 13.
    Naka, T., Shiba, K., Sakamoto, N.: A two-dimensional compartment model for the reaction-diffusion system of acetylcholine in the synaptic cleft at the neuromuscular junction. Biosystems 41(1), 17–27 (1997)CrossRefGoogle Scholar
  14. 14.
    Cheng, Y., Suen, J.K., Radi, Z., Bond, S.D., Holst, M.J., McCammon, J.A.: Continuum simulations of acetylcholine diffusion with reaction-determined boundaries in neuromuscular junction models. Biophys. Chem. 127(3), 129–139 (2007)CrossRefGoogle Scholar
  15. 15.
    Chang, R.: Physical Chemistry for the Biosciences. University Science Books, Sausalito (2005)Google Scholar
  16. 16.
    Gillespie, D.T.: A rigorous derivation of the chemical master equation. Phys. A 188(13), 404–425 (1992)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Pierobon, M., Akyildiz, I.F.: Information capacity of diffusion-based molecular communication in nanonetworks. In: Proceedings of 2011 IEEE INFOCOM 2011, pp. 506–510, April 2011Google Scholar
  18. 18.
    Pierobon, M., Akyildiz, I.F.: A physical end-to-end model for molecular communication in nanonetworks. IEEE J. Sel. Areas Commun. 28(4), 602–611 (2010)CrossRefGoogle Scholar
  19. 19.
    Debnath, L.: Nonlinear Partial Differential Equations for Scientists and Engineers, 2nd edn. Birkhaeuser, Boston (2005)CrossRefzbMATHGoogle Scholar
  20. 20.
    Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58(1), 35–55 (2007)CrossRefGoogle Scholar
  21. 21.
    Iyengar, K.A., Harris, L.A., Clancy, P.: Accurate implementation of leaping in space: the spatial partitioned-leaping algorithm. J. Chem. Phys. 132(9), 094101 (2010)CrossRefGoogle Scholar
  22. 22.
    Andrews, S.S., Bray, D.: Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol. 1(3), 137 (2004)CrossRefGoogle Scholar
  23. 23.
    Bernstein, D.: Simulating mesoscopic reaction-diffusion systems using the Gillespie algorithm. Phys. Rev. E 71(4), 041103 (2005)CrossRefMathSciNetGoogle Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada

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