Boosting the Immune System

  • Chris McEwan
  • Emma Hart
  • Ben Paechter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or modelling biologically plausible dynamical systems, with little overlap between. Although the balance is latterly beginning to be redressed (e.g. [18]), we propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction. This paper outlines how an inappropriate interpretation of Perelson’s shape-space formalism has largely contributed to this dichotomy, as it neither scales to machine-learning requirements nor makes any operational distinction between signals and context.

We illustrate these issues and attempt to derive both a more biologically plausible and statistically solid foundation for an online, unsupervised artificial immune system. By extending a mathematical model of immunological tolerance, and grounding it in contemporary machine learning, we minimise any recourse to “reasoning by metaphor” and demonstrate one view of how both research agendas might still complement each other.


Immunological Tolerance Immune Network Clonal Selection Algorithm Peripheral Immune System Immune Repertoire 
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.
    Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973. Springer, Heidelberg (2000)Google Scholar
  2. 2.
    Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? Lecture Notes in Computer Science, 1540. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Carneiro, J., Coutinho, A., Faro, J., Stewart, J.: A model of the immune network with b-t cell co-operation. i - prototypical structures and dynamics. Journal of Theoretical Biology 182, 513–529 (1996)CrossRefGoogle Scholar
  4. 4.
    Carneiro, J., Coutinho, A., Stewart, J.: A model of the immune network with b-t cell co-operation. ii - the simulation of ontogenisis. Journal of Theoretical Biology 182, 531–547 (1996)CrossRefGoogle Scholar
  5. 5.
    Carneiro, J., Stewart, J.: Rethinking shape space: Evidence from simulated docking suggests that steric shape complementarity is not limiting for antibody-antigen recognition and idiotypic interactions. J. Theor. Biol. 169, 391–402 (1994)CrossRefGoogle Scholar
  6. 6.
    Cohen, I.R., Segel, L.A.: Design Principles for the Immune System and Other Distributed Autonomous Systems. Oxford University Press, Oxford (2001)Google Scholar
  7. 7.
    Ferrer, R., Cancho, I., Sole, R.: The small-world of human language. In: Proceedings of the Royal Society of London (2001)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: A decision theoretic generalisation of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Friedman, J.H.: On bias variance 0-1 loss and the curse-of-dimensionality. Data Min. Knowl. Discov. 1, 55–77 (1997)CrossRefGoogle Scholar
  10. 10.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  11. 11.
    Janeway, C.A., Travers, P., Walport, M., Schlomchik, M.: Immunobiology, Garland (2001)Google Scholar
  12. 12.
    Jerne, N.K.: The generative grammer of the immune system. Nobel Lecture (1984)Google Scholar
  13. 13.
    Mcewan, C., Hart, E., Paechter, B.: Revisiting the central and peripheral immune system. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    McEwan, C., Hart, E., Paechter, B.: Towards a model of immunological tolerance and autonomous learning. Natural Computing (submitted, 2008)Google Scholar
  15. 15.
    Barabasi, A.-L., Oltvai, Z.N.: Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics 5, 101–113 (2004)CrossRefGoogle Scholar
  16. 16.
    Stewart, J., Carneiro, J.: Artificial Immune Systems and their Applications. In: The central and the peripheral immune system: What is the relationship?, pp. 47–64. Springer, Heidelberg (1998)Google Scholar
  17. 17.
    Stibor, T., Timmis, J., Eckert, C.: On the use of hyperspheres in artificial immune systems as antibody recognition regions. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163. Springer, Heidelberg (2006)Google Scholar
  18. 18.
    Timmis, J., Andrews, P., Owens, N., Clark, E.: An interdisciplinary perspective on artificial immune systems. Evolutionary Intelligence 1(1), 5–26 (2008)CrossRefGoogle Scholar
  19. 19.
    Varela, F.J., Coutinho, A.: Second generation immune networks. Immunology Today 12(5), 159–166 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chris McEwan
    • 1
  • Emma Hart
    • 1
  • Ben Paechter
    • 1
  1. 1.Napier UniversityEdinburghScotland

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