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Modelling Containment Mechanisms in the Immune System for Applications in Engineering

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Artificial Immune Systems (ICARIS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6825))

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Granuloma Formation

Granuloma formation is a complex process involving a variety of mechanisms acting in concert to afford an inflammatory lesion that is able to contain and destroy intracellular pathogens. While it is crucial for host defence, inappropriate granulomatous inflammation can also damage the host. Granuloma formation is comprised of four main steps : (1) the triggering of T cells by antigen presenting cells, represented by alveolar macrophages and dendritic cells; (2) the release of cytokines and chemokines by macrophages, activated lymphocytes and dendritic cells. Cytokines and chemokines attract and retain in the lung the immuno-inflammatory cell populations in the lung, inducing their survival and proliferation at the site of ongoing inflammation, favouring (3) the stable and dynamic accumulation of immunocompetent cells and the formation of the organised structure of the granuloma. In granulomatous diseases, the last phase (4) of granuloma formation generally ends in fibrosis. Granuloma formation is initiated when an infectious diseases enters the host. Macrophages will ‘eat′ or engulf bacteria to prevent it from spreading. However, bacteria will infect macrophages and duplicate. Despite the fact that macrophages are able to stop the infection, bacteria will use macrophages as a ‘taxi′ to spread the disease within the host leading to cell lysis or the breaking down of the structure of the cell. Infected macrophages then will emit a signal which indicates that they have been infected and this signal will lead other macrophages to move to the site of infection, to form a ring around the infected macrophages thus isolating the infected cells from the uninfected cells. This will finally lead to the formation of a granuloma that represents a chronic inflammatory response initiated by various infectious and non-infectious agents.

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References

  1. Stepney, S., Smith, R.E., Timmis, J., Tyrrell, A.M., Neal, M.J., Hone, A.N.W.: Conceptual frameworks for artificial immune systems. International Journal of Unconventional Computing 1, 315–338 (2005)

    Google Scholar 

  2. Timmis, J., Hart, E., Hone, A., Neal, M., Robins, A., Stepney, S., Tyrrell, A.: Immuno-engineering. In: 2nd International Conference on Biologically Inspired Collaborative Computing. IFIP, vol. 268, pp. 3–17 (2008)

    Google Scholar 

  3. Andrews, P.S., Polack, F.A.C., Sampson, A.T., Stepney, S., Timmis, J.: The cosmos process, version 0.1: A process of the modelling and simulation of complex systems (2010)

    Google Scholar 

  4. Fowler, M.: UML Distilled. Addisson-Wesley, London (2004)

    Google Scholar 

  5. Gilbert, N.: Agent-based models. SAGE Publications, Thousand Oaks (2008)

    Book  Google Scholar 

  6. Ismail, A.R., Timmis, J.: Towards self-healing swarm robotic systems inspired by granuloma formation. In: Special Session: Complex Systems Modelling and Simulation, part of ICECCS 2010, pp. 313–314. IEEE, Los Alamitos (2010)

    Google Scholar 

  7. Forrest, S., Beauchemin, C.: Computer immunology. Immunological Reviews 216, 176–197 (2007)

    Article  Google Scholar 

  8. Segovia-Juarez, J.L., Ganguli, S., Kirschner, D.: Identifying control mechanisms of granuloma formation during m. tuberculosis infection using an agent-based model. Theoretical Biology 231, 357–376 (2004)

    Article  MathSciNet  Google Scholar 

  9. Timmis, J., Tyrrell, A., Mokhtar, M., Ismail, A.R., Owens, N., Bi, R.: An artificial immune system for robot organisms. In: Levi, P., Kernbach, S. (eds.) Symbiotic Multi-Robot Organisms. Cognitive Systems Monographs, vol. 7, pp. 279–302. Springer, Heidelberg (2010)

    Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Ismail, A.R., Timmis, J. (2011). Modelling Containment Mechanisms in the Immune System for Applications in Engineering. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-22371-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22370-9

  • Online ISBN: 978-3-642-22371-6

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