Swarm-Based Simulations for Immunobiology

What Can Agent-Based Models Teach Us about the Immune System?
  • Christian Jacob
  • Vladimir Sarpe
  • Carey Gingras
  • Rolando Pajon Feyt
Part of the Intelligent Systems Reference Library book series (ISRL, volume 11)


In this contribution, we present a computer model of information processing within a highly distributed biological system of the human body, which is orchestrated over multiple scales of time and space: the immune system. We consider the human body and its environment as a well-orchestrated system of interacting swarms: swarms of cells, swarms of messenger molecules, swarms of bacteria, and swarms of viruses. Utilizing swarm intelligence techniques, we present three virtual simulations and experiments 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 compartments 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 in combination with primary and secondary collective responses after viral infection. We also model, simulate, and visualize key response patterns encountered during bacterial infection. As a third model we consider the complement system, for which we present initial simulation results. We consider these in-silico experiments and their associated modeling environments as an essential step towards hierarchical whole-body simulations of the immune system, both for educational and research purposes.


Complement System Viral Antigen Clonal Selection Complement Protein 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.


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  1. 1.
    Abbas, A.K., Lichtman, A.H.: Basic Immunology - Functions and Disorders of the Immune System. W. B. Saunders Company, Philadelphia (2001)Google Scholar
  2. 2.
    Atamas, S.P.: Self-organization in computer simulated selective systems. BioSystems 39, 143–151 (1996)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Biesma, D.H., Hannema, A.J., Van Velzen-Blad, H., Mulder, L., Van Zwieten, R., Kluijt, I., Roos, D.: A family with complement factor d deficiency. J. Clin. Invest. 108(2), 233–240 (2001)Google Scholar
  6. 6.
    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
  7. 7.
    Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton Studies in Complexity. Princeton University Press, Princeton (2003)zbMATHGoogle Scholar
  11. 11.
    Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton Studies in Complexity. Princeton University Press, Princeton (2003)zbMATHGoogle Scholar
  12. 12.
    Castiglione, F., Mannella, G., Motta, S., Nicosia, G.: A network of cellular automata for the simulation of the immune system. International Journal of Modern Physics C 10(4), 677–686 (1999)CrossRefGoogle Scholar
  13. 13.
    Celada, F., Seiden, P.E.: A computer model of cellular interactions in the immune system. Immunology Today 13(2), 56–62 (1992)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Clancy, J.: Basic Concepts in Immunology - A Student’s Survival Guide. McGraw-Hill, New York (1998)Google Scholar
  16. 16.
    Farmer, J.D., Packard, N.H.: The immune system, adaptation, and machine learning. Physica D 22, 187–204 (1986)MathSciNetCrossRefGoogle 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., Bentley, P., Timmis, J. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 220–233. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Hanegraaff, W.: Simulating the Immune System. Master’s thesis, Department of Computational Science, University of Amsterdam, Amsterdam, The Netherlands (September 2001)Google Scholar
  20. 20.
    Hoar, R., Penner, J., Jacob, C.: Transcription and evolution of a virtual bacteria culture. In: IEEE Congress on Evolutionary Computation. IEEE Press, Canberra (2003)Google Scholar
  21. 21.
    Jacob, C., Barbasiewicz, A., Tsui, G.: Swarms and genes: Exploring λ-switch gene regulation through swarm intelligence. In: Congress on Evolutionary Computation. IEEE Press, Vancouver (2006)Google Scholar
  22. 22.
    Jacob, C., Burleigh, I.: Biomolecular swarms: An agent-based model of the lactose operon. Natural Computing 3(4), 361–376 (December 2004)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Jacob, C., Litorco, J., Lee, L.: Immunity through swarms: Agent-based simulations of the human immune system. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 400–412. Springer, Heidelberg (2004),,11855,4-40109-22-34448379-0,00.html CrossRefGoogle Scholar
  24. 24.
    Jacob, C., von Mammen, S.: Swarm grammars: growing dynamic structures in 3d agent spaces. Digital Creativity 18(1), 54–64 (2007), CrossRefGoogle Scholar
  25. 25.
    Jacob, C., Steil, S., Bergmann, K.: The swarming body: Simulating the decentralized defenses of immunity. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 52–65. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Janeway, C.A., Travers, P., Walport, M., Shlomchik, M.J.: Immunobiology: The Immune System in Health and Disease, 6th edn. Garland Science, New York (2005), Google Scholar
  27. 27.
    Johnson, S.: Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner, New York (2001)Google Scholar
  28. 28.
    Kleinstein, S.H., Seiden, P.E.: Simulating the immune system. Computing in Science & Engineering 2(4), 69–77 (2000)CrossRefGoogle Scholar
  29. 29.
    Kuby, J., Goldsby, R.A., Kindt, T.J., Osborne, B.A.: Immunology, 6th edn. W. H. Freeman and Company, New York (2003)Google Scholar
  30. 30.
    von Mammen, S., Jacob, C.: Genetic swarm grammar programming: Ecological breeding like a gardener. In: IEEE Congress on Evolutionary Computation, Singapore (September 25-28, 2007)Google Scholar
  31. 31.
    von Mammen, S., Jacob, C.: The spatiality of swarms. In: Artificial Life XI, 11th International Conference on the Simulation and Synthesis of Living Systems, Winchester, UK (2008)Google Scholar
  32. 32.
    von Mammen, S., Jacob, C.: The evolution of swarm grammars: Growing trees, crafting art and bottom-up design. IEEE Computational Intelligence Magazine (2008) (under review)Google Scholar
  33. 33.
    Meier-Schellersheim, M., Fraser, I., Klauschen, F.: Multi-scale modeling in cell biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 1(1), 4–14 (2009)CrossRefGoogle Scholar
  34. 34.
    Muller-Eberhard, H.J.: Complement. Annu. Rev. Biochem. 44, 697–724 (1975)CrossRefGoogle Scholar
  35. 35.
    Noble, D.: The Music of Life. Oxford University Press, Oxford (2006), Google Scholar
  36. 36.
    Nossal, G.J.: Life, death and the immune system. Scientific American, 53–62 (September 1993)Google Scholar
  37. 37.
    Parham, P.: The Immune System. Garland Publishing, New York (2000); in my home library since 2003Google Scholar
  38. 38.
    Penner, J., Hoar, R., Jacob, C.: Bacterial chemotaxis in silico. In: ACAL 2003, First Australian Conference on Artificial Life, Canberra, Australia (2003)Google Scholar
  39. 39.
    Pogson, M., Smallwood, R., Qwarnstrom, E., Holcombe, M.: Formal agent-based modelling of intracellular chemical interactions. BioSystems (2006)Google Scholar
  40. 40.
    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
  41. 41.
    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
  42. 42.
    Rössler, O., Lutz, R.: A decomposable continuous immune network. BioSystems 11, 281–285 (1979)CrossRefGoogle Scholar
  43. 43.
    Salzberg, S., Searls, D., Kasif, S. (eds.): Computational Methods in Molecular Biology, New Comprehensive Biochemistry, vol. 32. Elsevier, Amsterdam (1998)Google Scholar
  44. 44.
    Smith, L.C.: The ancestral complement system in sea urchins. Immunological Reviews 180, 16–34 (2001)CrossRefGoogle Scholar
  45. 45.
    Sompayrac, L.: How the Immune System Works, 2nd edn. Blackwell Publishing, Malden (2003)Google Scholar
  46. 46.
    Spector, L., Klein, J., Perry, C., Feinstein, M.: Emergence of collective behavior in evolving populations of flying agents. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 61–73. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  47. 47.
    Tarakanov, A., Dasgupta, D.: A formal model of an artificial immune system. BioSystems 55, 151–158 (2000)CrossRefGoogle Scholar
  48. 48.
    Tay, J.C., Jhavar, A.: Cafiss: a complex adaptive framework for immune system simulation. In: SAC 2005: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 158–164. ACM Press, New York (2005)CrossRefGoogle Scholar
  49. 49.
    Walport, M.J.: Complement. first of two parts. N. Engl. J. Med. 344, 1058–1066 (2001)CrossRefGoogle Scholar
  50. 50.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Jacob
    • 1
    • 2
  • Vladimir Sarpe
    • 1
  • Carey Gingras
    • 1
  • Rolando Pajon Feyt
    • 3
  1. 1.Dept. of Computer ScienceUniversity of CalgaryCanada
  2. 2.Dept. of Biochemistry and Molecular BiologyUniversity of CalgaryCanada
  3. 3.Center for Immunobiology and Vaccine DevelopmentChildren’s Hospital Oakland Research InstituteOaklandUSA

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