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Networking genetic regulation and neural computation: Directed network topology and its effect on the dynamics

Andreas Grönlund*

  • Department of Physics, Umeå University, 901 87 Umeå, Sweden

  • *Electronic address: gronlund@tp.umu.se
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Phys. Rev. E 70, 061908 – Published 20 December, 2004

DOI: https://doi.org/10.1103/PhysRevE.70.061908

Abstract

Two different types of directed networks are investigated, transcriptional regulation networks and neural networks. The directed network structure is studied and is also shown to reflect the different processes taking place on the networks. The distribution of influence, identified as the the number of downstream vertices, are used as a tool for investigating random vertex removal. In the transcriptional regulation networks we observe that only a small number of vertices have a large influence. The small influences of most vertices limit the effect of a random removal to, in most cases, only a small fraction of vertices in the network. The neural network has a rather different topology with respect to the influence, which are large for most vertices. To further investigate the effect of vertex removal we simulate the biological processes taking place on the networks. Opposed to the presumed large effect of random vertex removal in the neural network, the high density of edges in conjunction with the dynamics used makes the change in the state of the system to be highly localized around the removed vertex.

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References (26)

  1. A.-L. Barabási and R. Albert, Science 286, 509 (1999).
  2. M. E. J. Newman, Phys. Rev. Lett. 89, 208701 (2002).
  3. L. Freeman, Sociometry 40, 35 (1977).
  4. M. Barthélemy, Eur. Phys. J. B38, 163 (2004).
  5. M. E. J. Newman, e-print cond-mat/0309045.
  6. M. E. J. Newman, SIAM Rev. 45, 167 (2003).
  7. S. N. Dorogovtsev et al., Adv. Phys. 51, 1079 (2002).
  8. S. Bornholdt and H. G. Schuster, Handbook of Graphs and Networks—From the Genome to the Internet (Wiley-VCH, Berlin, 2002).
  9. J. G. White, E. Southgate, J. N. Thompson, and S. Brenner, Philos. Trans. R. Soc. London, Ser. A 314, 1 (1986).
  10. S. Shen-Orr et al., Nat. Genet. 31, 64 (2002).
  11. T. I. Lee, et al., Science 298, 799 (2002).
  12. Neuroscience, 3rd ed., edited by D. Purves (Sinauer Associates Inc., 2004).
  13. S. Maslov and K. Sneppen, Science 296, 910 (2002).
  14. E. Ravasz et al., Science 297, 1553 (2002).
  15. M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002).
  16. F. Radicchi et al., Proc. Natl. Acad. Sci. U.S.A.101, 2658 (2004).
  17. A. Grönlund and P. Holme, Phys. Rev. E 70, 036108 (2004).
  18. L. H. Hartwell et al., Nature (London) 402, C47 (1999).
  19. M. E. Csete and J. C. Doyle, Science 295, 1664 (2002).
  20. J. Ihmels et al., Nat. Genet. 31, 370 (2002).
  21. S. Bornholdt, Biol. Chem. 382, 1289 (2001).
  22. N. Lin, Foundations of Social Research (McGraw-Hill, New York, 1976).
  23. K. Stuart, P. Carsten, S. Björn, and T. Carl, Proc. Natl. Acad. Sci. U.S.A. 100, 14 796 (2003).
  24. K. S, and W. JN, in Systems, Man and Cybernetics, 2003 IEEE International Conference (Washington, DC, 2003), Vol. 4, pp. 3969–3975.
  25. W. S. McCulloch and W. Pitts, Bull. Math. Biophys. 5, 115 (1943).
  26. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, Redwood City, CA, 1991).

Outline

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Phys. Rev. E 70, 061908– Published 20 December, 2004
  • Received 11 June 2004

© 2004 American Physical Society

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