Skip to main content

An Agent-Based Approach to Immune Modelling

  • Conference paper
Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3980))

Included in the following conference series:


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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Burns, J.: Emergent networks in immune system shape space. PhD thesis, Dublin City University, School of Computing (2005)

    Google Scholar 

  2. Germain, R.N.: The Art of the Probable: System Control in the Adaptive Immune System. Science 239(5528), 240–245 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Lemahieu, J.C.: Le systeme immunitaire. Immunology courses [French] (2005) available online at, (last access on December 14, 2005)

  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)

    Article  Google Scholar 

  6. Decoster, A., Lemahieu, J.C.: Les retrovirus. Immunology courses [French] (2005) available online at, (last access on December 14, 2005)

  7. Wooldridge, M., Jennings, N.: Intelligent agents: Theory and practice. The Knowledge Engineering Review 2(10), 115–152 (1995)

    Article  Google Scholar 

  8. Durfee, E.H.: Scaling up agent coordination strategies. Computer 34(7), 39–46 (2001)

    Article  Google Scholar 

  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. Durfee, E.H.: Coordination of distributed problem solvers. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  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. Kari, J.: Theory of cellular automata: A survey. Theoretical Computer Science 334(2005), 3–35 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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. Srinivasan, A., Mascagni, M., Ceperley, D.: Testing parallel random number generators. Parallel Computing 29(2003), 69–94 (2003)

    Article  MathSciNet  Google Scholar 

  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. 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. Gropp, W., Lusk, E., Skjellum, A.: Using MPI-2: Advanced Features of the Message Passing Interface. MIT Press, Cambridge (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Perrin, D., Ruskin, H.J., Burns, J., Crane, M. (2006). An Agent-Based Approach to Immune Modelling. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34070-6

  • Online ISBN: 978-3-540-34071-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics