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.
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References
Louzoun, Y.: The evolution of mathematical immunology. Imm. Rev. 216, 9–20 (2007)
Gruber, J.: Models of Immune Systems: The Use of Differential Equations, http://www.lymenet.de/literatur/immundif.html (last accessed June 13, 2011)
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)
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)
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)
Østreng, W.: Reductionism versus Holism – Contrasting Approaches? In: Consilience. Interdisciplinary Communications 2005/2006. Centre for Advanced Study, Oslo, pp. 11–14 (2007)
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)
Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)
Kitano, H.: Systems biology: A brief overview. Science 295, 1662–1664 (2002)
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)
Robinson, S.: Simulation: The Practice of Model Development and Use. John Wiley and sons, Ltd. (2004)
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)
Babulak, E., Wang, M.: 1. In: Discrete Event Simulation: State of the Art, pp. 1–8. InTech (2010)
Banks, J., Carson, J., Nelson, B., Nicol, D.: Discrete-event system simulation, 4th edn. Pearson (2005)
Macal, C.M.: To agent-based simulation from system dynamics. In: Proc. of the 2010 Winter Simulation Conference (2010)
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)
Metropolis, N., Ulam, S.: The Monte Carlo method. J. Amer. Stat. Assoc. 44, 335–341 (1949)
Forrester, J.W.: Industrial Dynamics. Pegasus Communications (1961)
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)
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)
Siebers, P.O., Aickelin, U.: Introduction to Multi-Agent Simulation. In: Encyclopaedia of Decision Making and Decision Support Technologies, pp. 554–564 (2007)
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)
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)
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)
Figge, M.T.: Stochastic discrete event simulation of germinal center reactions. Phys. Rev. E 71, 51907 (2005)
Wolfram, S.: Statistical mechanics of cellular automata. Rev. Mod. Phys. 5, 601–644 (1983)
Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation. In: Proc. of the 2005 Winter Simulation Conference (2005)
ImmunoGrid. The European Virtual Immune System Project, www.immunogrid.eu (last accessed July 15, 2012)
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)
Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol (Inst. Pasteur) 125C, 73–89 (1974)
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)
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)
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)
Daigle, J.: Human immune system simulation: A survey of current approaches. Georgia State University (2006)
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)
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Figueredo, G.P., Siebers, PO., Aickelin, U., Foan, S. (2012). A Beginner’s Guide to Systems Simulation in Immunology. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_5
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DOI: https://doi.org/10.1007/978-3-642-33757-4_5
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