Metabolic Systemic Computing: Exploiting Innate Immunity within an Artificial Organism for On-line Self-Organisation and Anomaly Detection

  • Erwan Le Martelot
  • Peter J. Bentley


Previous work suggests that innate immunity and representations of tissue can be useful when combined with artificial immune systems. Here we provide a new implementation of tissue for artificial immune systems using systemic computation, a new model of computation and corresponding computer architecture based on a systemics world-view and supplemented by the incorporation of natural characteristics. We show using systemic computation how to create an artificial organism, a program with metabolism that eats data, expels waste, self-organise cells based on the nature of its food and emits danger signals suitable for an artificial immune system. The implementation is tested by application to two standard machine learning sets and shows excellent abilities to recognise anomalies in its diet as well as a consistent datawise self-organisation.


Systemic computation Tissue Innate immunity Anomaly detection Self-organisation Danger theory Artificial organism Artificial metabolism Artificial immune system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aickelin, U., Greensmith, J.: Sensing danger: innate immunology for intrusion detection. Elsevier information security technical report, pp. 218–227. Elsevier, Amsterdam (2007)Google Scholar
  2. 2.
    Matzinger, P.: Tolerance, danger and the extended family. Annu. Rev. Immunol. 12, 991–1045 (1994)Google Scholar
  3. 3.
    Bentley, P.J., Greensmith, J., Ujjin, S.: Two ways to grow tissue for artificial immune systems. In: Proc. of the Fourth Intl. Conf. on Artificial Immune Systems (ICARIS 2005). LNCS 3627, pp. 139–152. Springer, New York (2005)Google Scholar
  4. 4.
    Bentley, P.J.: Systemic computation: a model of interacting systems with natural characteristics. Int. J. Parallel Emergent Distrib. Syst. 22(2), 103–121 (2007)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Breast Cancer Wisconsin (Diagnostic) Dataset: Creator: Wolberg, W.H., Donor: Mangasarian, O. UCI machine learning repository. (1992)
  6. 6.
    Wine Dataset: original owners: Forina, M., et al., PARVUS, Donor: Aeberhard, S. UCI machine learning repository. (1991)
  7. 7.
    Tempesti, G., Roggen, D., Sanchez, E., Thoma, Y.: A POEtic architecture for bio-inspired hardware. In: Proc. of the 8th Intl. Conf. on the Simulation and Synthesis of Living Systems (Artificial Life VIII), pp. 111–115. MIT, Cambridge (2002)Google Scholar
  8. 8.
    Thoma, Y., Tempesti, G., Sanchez, E., Moreno Arostegui, J.-M.: POEtic: an electronic tissue for bio-inspired cellular applications. BioSystems 76, 191–200 (2004)CrossRefGoogle Scholar
  9. 9.
    Wallenta, C., Kim, J., Bentley, P.J., Hailes, S.: Detecting interest cache poisoning in sensor networks using an artificial immune algorithm. J. Appl. Intell. doi: 10.1007/s10489-008-0132-0 (2008)Google Scholar
  10. 10.
    von Neumann, J.: The Theory of Self-reproducing Automata. University of Illinois Press, Champaign (1966)Google Scholar
  11. 11.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)MATHGoogle Scholar
  12. 12.
    Holland, J.H.: Emergence, from Chaos to Order. Oxford University Press, Oxford (1998)MATHGoogle Scholar
  13. 13.
    Adamatzky, A.: Computing in Nonlinear Media and Automata Collectives. Institute of Physics, Bristol (2001)MATHCrossRefGoogle Scholar
  14. 14.
    Arvind, D.K., Wong, K.J.: Speckled computing: disruptive technology for networked information appliances. In: Proc. of the IEEE Intl. Symposium on Consumer Electronics (ISCE’04), pp. 219–223. IEEE, Piscataway (2004)CrossRefGoogle Scholar
  15. 15.
    Le Martelot, E., Bentley, P.J., Lotto, R.B.: A systemic computation platform for the modelling and analysis of processes with natural characteristics. In: Proc. of 9th Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 2809–2816. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Le Martelot, E., Bentley, P.J., Lotto, R.B.: Exploiting natural asynchrony and local knowledge within systemic computation to enable generic neural structures. In: Proc. of 2nd International Workshop on Natural Computing (IWNC 2007), Nagoya, 10–12 December 2007Google Scholar
  17. 17.
    Le Martelot, E., Bentley, P.J., Lotto, R.B.: Crash-Proof systemic computing: a demonstration of native fault-tolerance and self-maintenance. In: Proc. of 4th IASTED International Conference on Advances in Computer Science and Technology (ACST 2008). ACTA, Anaheim (2008)Google Scholar
  18. 18.
    Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Proc. of the Fourth Intl. Conf. on Artificial Immune Systems (ICARIS 2005). LNCS 3627, pp. 153–167. Springer, New York (2005)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Engineering DepartmentUniversity College LondonLondonUK
  2. 2.Computer Science DepartmentUniversity College LondonLondonUK

Personalised recommendations