The Swarming Body: Simulating the Decentralized Defenses of Immunity

  • Christian Jacob
  • Scott Steil
  • Karel Bergmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)


We consider the human body as a well-orchestrated system of interacting swarms. Utilizing swarm intelligence techniques, we present our latest virtual simulation and experimentation environment, IMMS:VIGO::3D, to explore key aspects of the human immune system. Immune system cells and related entities (viruses, bacteria, cytokines) are represented as virtual agents inside 3-dimensional, decentralized and compartmentalized environments that represent primary and secondary lymphoid organs as well as vascular and lymphatic vessels. Specific immune system responses emerge as by-products from collective interactions among the involved simulated ‘agents’ and their environment. We demonstrate simulation results for clonal selection and primary and secondary collective responses after viral infection, as well as the key response patterns encountered during bacterial infection. We see this simulation environment as an essential step towards a hierarchical whole-body simulation of the immune system, both for educational and research purposes.


Clonal Selection Swarm Intelligence Tissue Area Complex Adaptive System Human Immune System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Przybyla, D., Miller, K., Pegah, M.: A holistic approach to high-performance computing: xgrid experience. In: ACM (ed.) Proceedings of the 32nd annual ACM SIGUCCS conference on User services, pp. 119–124 (2004)Google Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  3. 3.
    Farmer, J.D., Packard, N.H.: The immune system, adaptation, and machine learning. Physica D 22, 187–204 (1986)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Bagley, R.J., Farmer, J.D., Kauffman, S.A., Packard, N.H., Perelson, A.S., Stadnyk, I.M.: Modeling adaptive biological systems. BioSystems 23, 113–138 (1989)CrossRefGoogle Scholar
  5. 5.
    Rössler, O., Lutz, R.: A decomposable continuous immune network. BioSystems 11, 281–285 (1979)CrossRefGoogle Scholar
  6. 6.
    Salzberg, S.L., Searls, D.B., Kasif, S. (eds.): Computational Methods in Molecular Biology. New Comprehensive Biochemistry, vol. 32. Elsevier, Amsterdam (1998)zbMATHGoogle Scholar
  7. 7.
    Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    Hanegraaff, W.: Simulating the immune system. Master’s thesis, Department of Computational Science, University of Amsterdam, Amsterdam, The Netherlands (2001)Google Scholar
  9. 9.
    Bezzi, M., Celada, F., Ruffo, S., Seiden, P.E.: The transition between immune and disease states in a cellular automaton model of clonal immune response. Physica A 245, 145–163 (1997)CrossRefGoogle Scholar
  10. 10.
    Tay, J.C., Jhavar, A.: Cafiss: a complex adaptive framework for immune system simulation. In: Preneel, B., Tavares, S. (eds.) SAC 2005. LNCS, vol. 3897, pp. 158–164. Springer, Heidelberg (2006)Google Scholar
  11. 11.
    Tarakanov, A., Dasgupta, D.: A formal model of an artificial immune system. BioSystems 55, 151–158 (2000)CrossRefGoogle Scholar
  12. 12.
    Celada, F., Seiden, P.E.: A computer model of cellular interactions in the immune system. Immunology Today 13(2), 56–62 (1992)CrossRefGoogle Scholar
  13. 13.
    Celada, F., Seiden, P.E.: Affinity maturation and hypermutation in a simulation of the humoral immune response. European Journal of Immunology 26, 1350–1358 (1996)CrossRefGoogle Scholar
  14. 14.
    Atamas, S.P.: Self-organization in computer simulated selective systems. BioSystems 39, 143–151 (1996)CrossRefGoogle Scholar
  15. 15.
    Kleinstein, S.H., Seiden, P.E.: Simulating the immune system. In: Computing in Science & Engineering, pp. 69–77 (July/August 2000)Google Scholar
  16. 16.
    Puzone, R., Kohler, B., Seiden, P., Celada, F.: Immsim, a flexible model for in machina experiments on immune system responses. Future Generation Computer Systems 18(7), 961–972 (2002)zbMATHCrossRefGoogle Scholar
  17. 17.
    Guo, Z., Han, H.K., Tay, J.C.: Sufficiency verification of hiv-1 pathogenesis based on multi-agent simulation. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 305–312. ACM Press, New York (2005)Google Scholar
  18. 18.
    Guo, Z., Tay, J.C.: A comparative study on modeling strategies for immune system dynamics under HIV-1 infection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 220–233. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Johnson, S.: Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner, New York (2001)Google Scholar
  20. 20.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)zbMATHGoogle Scholar
  21. 21.
    Pogson, M., Smallwood, R., Qwarnstrom, E., Holcombe, M.: Formal agent-based modelling of intracellular chemical interactions. BioSystems (2006)Google Scholar
  22. 22.
    Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. In: Princeton Studies in Complexity, Princeton University Press, Princeton (2003)Google Scholar
  23. 23.
    Hoar, R., Penner, J., Jacob, C.: Transcription and evolution of a virtual bacteria culture. In: Congress on Evolutionary Computation, Canberra, Australia. IEEE Press, Los Alamitos (2003)Google Scholar
  24. 24.
    Penner, J., Hoar, R., Jacob, C.: Bacterial chemotaxis in silico. In: ACAL 2003, First Australian Conference on Artificial Life, Canberra, Australia (2003)Google Scholar
  25. 25.
    Burleigh, I., Suen, G., Jacob, C.: Dna in action! a 3d swarm-based model of a gene regulatory system. In: ACAL 2003, First Australian Conference on Artificial Life, Canberra, Australia (2003)Google Scholar
  26. 26.
    Jacob, C., Burleigh, I.: Biomolecular swarms: An agent-based model of the lactose operon. Natural Computing (in print, 2004)Google Scholar
  27. 27.
    Jacob, C., Barbasiewicz, A., Tsui, G.: Swarms and genes: Exploring λ-switch gene regulation through swarm intelligence. In: Congress on Evolutionary Computation, Vancouver, BC, Canada. IEEE Press, Los Alamitos (2006)Google Scholar
  28. 28.
    Nossal, G.J.: Life, death and the immune system. Scientific American, 53–62 (1993)Google Scholar
  29. 29.
    Burleigh, I.G.: A journey to the center of the cell. Master’s thesis, Department of Computer Science, University of Calgary, Calgary, Canada (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian Jacob
    • 1
    • 2
  • Scott Steil
    • 2
  • Karel Bergmann
    • 2
  1. 1.Dept. of Biochemistry & Molecular Biology, Faculty of Medicine 
  2. 2.Dept. of Computer Science, Faculty of ScienceUniversity of CalgaryCalgaryCanada

Personalised recommendations