An Agent-Based Approach to Immune Modelling

  • Dimitri Perrin
  • Heather J. Ruskin
  • John Burns
  • Martin Crane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


This study focuses on trying to understand why the range of experience with respect to HIV infection is so diverse, especially as regards to the latency period. The challenge is to determine what assumptions can be made about the nature of the experience of antigenic invasion and diversity that can be modelled, tested and argued plausibly. To investigate this, an agent-based approach is used to extract high-level behaviour which cannot be described analytically from the set of interaction rules at the cellular level. A prototype model encompasses local variation in baseline properties contributing to the individual disease experience and is included in a network which mimics the chain of lymphatic nodes. Dealing with massively multi-agent systems requires major computational efforts. However, parallelisation methods are a natural consequence and advantage of the multi-agent approach. These are implemented using the MPI library.


HIV immune response complex system agent-based parallelisation methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burns, J.: Emergent networks in immune system shape space. PhD thesis, Dublin City University, School of Computing (2005)Google Scholar
  2. 2.
    Germain, R.N.: The Art of the Probable: System Control in the Adaptive Immune System. Science 239(5528), 240–245 (2001)CrossRefGoogle Scholar
  3. 3.
    Jennings, N., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Autonomous agents and multi-agents systems 1(1), 7–38 (1998)CrossRefGoogle Scholar
  4. 4.
    Lemahieu, J.C.: Le systeme immunitaire. Immunology courses [French] (2005) available online at, (last access on December 14, 2005)
  5. 5.
    Klatzmann, D., Champagne, E., Chamaret, S., Gruest, J., Guetard, D., Hercend, T., Gluckman, J.C., Montagnier, L.: T-lymphocyte T4 molecule behaves as the receptor for human retrovirus LAV. Nature 312(5596), 767–768 (1984)CrossRefGoogle Scholar
  6. 6.
    Decoster, A., Lemahieu, J.C.: Les retrovirus. Immunology courses [French] (2005) available online at, (last access on December 14, 2005)
  7. 7.
    Wooldridge, M., Jennings, N.: Intelligent agents: Theory and practice. The Knowledge Engineering Review 2(10), 115–152 (1995)CrossRefGoogle Scholar
  8. 8.
    Durfee, E.H.: Scaling up agent coordination strategies. Computer 34(7), 39–46 (2001)CrossRefGoogle Scholar
  9. 9.
    Cammarata, S., McArthur, D., Steeb, R.: Strategies of cooperation in distributed problem solving. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI 1983), Karlsruhe, Germany (1983)Google Scholar
  10. 10.
    Durfee, E.H.: Coordination of distributed problem solvers. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  11. 11.
    Hayes-Roth, B., Hewett, M., Washington, R., Hewett, R., Seiver, A.: Distributing intelligence within an individual. In: Gasser, L., Huhns, M. (eds.) Distributed Artificial Intelligence, vol. II, pp. 385–412. Pitman Publishing and Morgan Kaufmann (1989)Google Scholar
  12. 12.
    Kari, J.: Theory of cellular automata: A survey. Theoretical Computer Science 334(2005), 3–35 (2005)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The Swarm simulation system: A toolkit for building multi-agent simulations. Working Paper 96-06-042, Santa Fe Institute (1996)Google Scholar
  14. 14.
    Press, W.H., Vetterling, W.T., Teukolsky, S.A., Flannery, B.P.: Numerical Recipes in C++: the art of scientific computing. Cambridge University Press, Cambridge (2002)Google Scholar
  15. 15.
    Srinivasan, A., Mascagni, M., Ceperley, D.: Testing parallel random number generators. Parallel Computing 29(2003), 69–94 (2003)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Hecquet, D., Ruskin, H.J., Crane, M.: Optimisation and parallelisation strategies for Monte Carlo simulation of HIV infection. Submitted to Computers in Biology and Medicine (2005)Google Scholar
  17. 17.
    Gropp, W., Lusk, E., Skjellum, A.: Using MPI: Portable Parallel Programming With the Message-Passing Interface, 2nd edn. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Gropp, W., Lusk, E., Skjellum, A.: Using MPI-2: Advanced Features of the Message Passing Interface. MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dimitri Perrin
    • 1
  • Heather J. Ruskin
    • 1
  • John Burns
    • 1
  • Martin Crane
    • 1
  1. 1.School of ComputingDublin City UniversityDublin 9Ireland

Personalised recommendations