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A Beginner’s Guide to Systems Simulation in Immunology

  • Grazziela P. Figueredo
  • Peer-Olaf Siebers
  • Uwe Aickelin
  • Stephanie Foan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7597)

Abstract

Some common systems modelling and simulation approaches for immune problems are Monte Carlo simulations, system dynamics, discrete-event simulation and agent-based simulation. These methods, however, are still not widely adopted in immunology research. In addition, to our knowledge, there is few research on the processes for the development of simulation models for the immune system. Hence, for this work, we have two contributions to knowledge. The first one is to show the importance of systems simulation to help immunological research and to draw the attention of simulation developers to this research field. The second contribution is the introduction of a quick guide containing the main steps for modelling and simulation in immunology, together with challenges that occur during the model development. Further, this paper introduces an example of a simulation problem, where we test our guidelines.

Keywords

Cellular Automaton Memory Cell System Simulation Simulation Approach System Dynamic Model 
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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References

  1. 1.
    Louzoun, Y.: The evolution of mathematical immunology. Imm. Rev. 216, 9–20 (2007)Google Scholar
  2. 2.
    Gruber, J.: Models of Immune Systems: The Use of Differential Equations, http://www.lymenet.de/literatur/immundif.html (last accessed June 13, 2011)
  3. 3.
    Eftimie, R., Bramson, J.L., Earn, D.J.: Interactions between the immune system and cancer: A brief review of non-spatial mathematical models. Bull. Math. Biol. (2010)Google Scholar
  4. 4.
    Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. In: Proc. of the National Academy of Sciences of the United States of America, vol. 99, pp. 7280–7287 (2002)Google Scholar
  5. 5.
    Sauro, H.M., Harel, D., Kwiatkowska, M., Shaffer, C.A., Uhrmacher, A.M., Hucka, M., Mendes, P., Strömback, L., Tyson, J.J.: Challenges for modeling and simulation methods in systems biology. In: Winter Simulation Conference, pp. 1720–1730 (2006)Google Scholar
  6. 6.
    Østreng, W.: Reductionism versus Holism – Contrasting Approaches? In: Consilience. Interdisciplinary Communications 2005/2006. Centre for Advanced Study, Oslo, pp. 11–14 (2007)Google Scholar
  7. 7.
    Fachada, N., Lopes, V., Rosa, A.: Agent-based modelling and simulation of the immune system: a review. In: EPIA 2007 - 13th Portuguese Conference on Artificial Intelligence (2007)Google Scholar
  8. 8.
    Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)CrossRefGoogle Scholar
  9. 9.
    Kitano, H.: Systems biology: A brief overview. Science 295, 1662–1664 (2002)CrossRefGoogle Scholar
  10. 10.
    Andrews, P.S., Polack, F.A.C., Sampson, A.T., Stepney, S., Timmis, J.: The CoSMoS process version 0.1: A process for the modelling and simulation of complex systems. Technical Report YCS-2010-453, Department of Computer Science, University of York (2010)Google Scholar
  11. 11.
    Robinson, S.: Simulation: The Practice of Model Development and Use. John Wiley and sons, Ltd. (2004)Google Scholar
  12. 12.
    Silva, P.S., Trigo, A., Varajão, J., Pinto, T.: Simulation – Concepts and Applications. In: Lytras, M.D., Ordonez de Pablos, P., Ziderman, A., Roulstone, A., Maurer, H., Imber, J.B. (eds.) WSKS 2010. CCIS, vol. 112, pp. 429–434. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Babulak, E., Wang, M.: 1. In: Discrete Event Simulation: State of the Art, pp. 1–8. InTech (2010)Google Scholar
  14. 14.
    Banks, J., Carson, J., Nelson, B., Nicol, D.: Discrete-event system simulation, 4th edn. Pearson (2005)Google Scholar
  15. 15.
    Macal, C.M.: To agent-based simulation from system dynamics. In: Proc. of the 2010 Winter Simulation Conference (2010)Google Scholar
  16. 16.
    Schieritz, N., Milling, P.M.: Modeling the forrest or modeling the trees: A comparison of system dynamics and agent based simulation. In: Proc. of the XXI Int. Conference of the System Dynamics Society (2003)Google Scholar
  17. 17.
    Metropolis, N., Ulam, S.: The Monte Carlo method. J. Amer. Stat. Assoc. 44, 335–341 (1949)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Forrester, J.W.: Industrial Dynamics. Pegasus Communications (1961)Google Scholar
  19. 19.
    Figueredo, G.P., Aickelin, U., Siebers, P.O.: Systems dynamics or agent-based modelling for immune simulation? In: Proc. of the Int. Conf. on AIS (2011)Google Scholar
  20. 20.
    Foan, S.J., Jackson, A.M., Spendlove, I., Aickelin, U.: Simulating the dynamics of T cell subsets throughout the lifetime. In: Proc. of the Int. Conf. on AIS (2011)Google Scholar
  21. 21.
    Siebers, P.O., Aickelin, U.: Introduction to Multi-Agent Simulation. In: Encyclopaedia of Decision Making and Decision Support Technologies, pp. 554–564 (2007)Google Scholar
  22. 22.
    Tako, A.A., Robinson, S.: Comparing model development in discrete event simulation and system dynamics. In: Rossetti, M.D., Hill, R., Dunkin, A., Ingalls, R.G. (eds.) Proc. of the 2009 Winter Simulation Conference, pp. 979–990 (2009)Google Scholar
  23. 23.
    Look, A.T., Schriber, T.J., Nawrocki, J.F., Murphy, W.H.: Computer simulation of the cellular immune response to malignant lymphoid cells: logic of approach, model design and laboratory verification. Immunology 43, 677–690 (1981)Google Scholar
  24. 24.
    Zand, M.S., Briggs, B., Bose, A., Vo, T.: Discrete event modeling of CD4+ memory T cell generation. The Journal of Immunology 173, 3763–3772 (2004)Google Scholar
  25. 25.
    Figge, M.T.: Stochastic discrete event simulation of germinal center reactions. Phys. Rev. E 71, 51907 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wolfram, S.: Statistical mechanics of cellular automata. Rev. Mod. Phys. 5, 601–644 (1983)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation. In: Proc. of the 2005 Winter Simulation Conference (2005)Google Scholar
  28. 28.
    ImmunoGrid. The European Virtual Immune System Project, www.immunogrid.eu (last accessed July 15, 2012)
  29. 29.
    Thorne, B.C., Bailey, A.M., Pierce, S.M.: Combining experiments with multi-cell agent-based modeling to study biological tissue patterning. Briefings in Bioinformatics 8, 245–257 (2007)CrossRefGoogle Scholar
  30. 30.
    Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol (Inst. Pasteur) 125C, 73–89 (1974)Google Scholar
  31. 31.
    Baltcheva, I., Codarri, L., Pantaleo, G., Boudec, J.Y.L.: Lifelong Dynamics of Human CD4 + CD25 +  Regulatory T Cells: Insights from in vivo Data and Mathematical Modeling. Journal of Theoretical Biology 266, 307–322 (2010)CrossRefGoogle Scholar
  32. 32.
    Kotiadis, K., Robinson, S.: Conceptual modelling: Knowledge acquisition and model abstraction. In: Madon, S.J., Hill, R.R., Mönch, L., Rose, O., Jefferson, T., Fowler, J.W. (eds.) Proc. of the 2008 Winter Simulation Conference, pp. 951–958 (2008)Google Scholar
  33. 33.
    Ulgen, O.M., Black, J.J., Johnsonbaugh, B., Klungle, R.: Simulation methodology - a practitioner’s perspective. Int. Journal of Industrial Engineering, Applications and Practice 1 (1994)Google Scholar
  34. 34.
    Daigle, J.: Human immune system simulation: A survey of current approaches. Georgia State University (2006)Google Scholar
  35. 35.
    Murray, J.M., Kaufmann, G.R., Hodgkin, P.D., Lewin, S.R., Kelleher, A.D., Davenport, M.P., Zaunders, J.: Naive T cells are maintained by thymic output in early ages but by proliferation without phenotypic change after twenty. Immunology and Cell Biology 81, 487–495 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Grazziela P. Figueredo
    • 1
  • Peer-Olaf Siebers
    • 1
  • Uwe Aickelin
    • 1
  • Stephanie Foan
    • 1
  1. 1.Intelligent Modelling and Analysis Research Group, School of Computer ScienceThe University of NottinghamUK

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