Eating Data Is Good for Your Immune System: An Artificial Metabolism for Data Clustering Using Systemic Computation

  • Erwan Le Martelot
  • Peter J. Bentley
  • R. Beau Lotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


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 AIS 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, clusters 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 a standard machine learning set and shows excellent abilities to recognise anomalies in its diet.


Food System Data Item Danger Signal Systemic Computation Adhesion Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aickelin, U., Greensmith, J.: Sensing Danger: Innate Immunology for Intrusion Detection. Elsevier Information Security Technical Report, pp. 218–227 (2007)Google Scholar
  2. 2.
    Matzinger, P.: Tolerance, Danger and the Extended Family. Annual Reviews in Immunology 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: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 139–152. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Bentley, P.J.: Systemic computation: A Model of Interacting Systems with Natural Characteristics. Int.J. Parallel, Emergent and Distributed Systems 22(2), 103–121 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Breast Cancer Wisconsin (Diagnostic) Data Set, Creator: Wolberg, W. H., Donor: Mangasarian, O., UCI Machine Learning Repository (1992),
  6. 6.
    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 Press, Cambridge (2002)Google Scholar
  7. 7.
    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
  8. 8.
    Wallenta, C., Kim, J., Bentley, P.J., Hailes, S.: Detecting Interest Cache Poisoning in Sensor Networks using an Artificial Immune Algorithm. Journal of Applied Intelligence (to appear, 2008)Google Scholar
  9. 9.
    von Neumann, J.: The theory of self-reproducing automata. Univ. of Illinois Press, Champaign (1966)Google Scholar
  10. 10.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Inc., Champaign (2002)zbMATHGoogle Scholar
  11. 11.
    Holland, J.H.: Emergence, From Chaos to Order. Oxford University Press, Oxford (1998)zbMATHGoogle Scholar
  12. 12.
    Adamatzky, A.: Computing in Nonlinear Media and Automata Collectives. Institute of Physics Publishing, Bristol (2001)zbMATHGoogle Scholar
  13. 13.
    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 2004), pp. 219–223 (2004)Google Scholar
  14. 14.
    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 Press, New York (2007)CrossRefGoogle Scholar
  15. 15.
    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) (2007)Google Scholar
  16. 16.
    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 press (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Erwan Le Martelot
    • 1
    • 3
  • Peter J. Bentley
    • 2
  • R. Beau Lotto
    • 3
  1. 1.Engineering DepartmentUniversity College LondonLondonUK
  2. 2.Computer Science DepartmentUniversity College LondonLondonUK
  3. 3.Institute of OphthalmologyUniversity College LondonLondonUK

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