The Value of Inflammatory Signals in Adaptive Immune Responses

  • Soumya Banerjee
  • Drew Levin
  • Melanie Moses
  • Frederick Koster
  • Stephanie Forrest
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6825)

Abstract

Cells of the immune system search among billions of healthy cells to find and neutralize a small number of infected cells before pathogens replicate to sufficient numbers to cause disease or death. The immune system uses information signals to accomplish this search quickly. Ordinary differential equations and spatially explicit agent-based models are used to quantify how capillary inflammation decreases the time it takes for cytotoxic T lymphocytes to find and kill infected cells. We find that the inflammation signal localized in a small region of infected tissue dramatically reduces search times, suggesting that these signals play an important role in the immune response, especially in larger animals.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Banerjee, S., Moses, M.E.: A hybrid agent based and differential equation model of body size effects on pathogen replication and immune system response. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 14–18. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Banerjee, S., Moses, M.: Modular RADAR: An immune system inspired search and response strategy for distributed systems. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds.) ICARIS 2010. LNCS, vol. 6209, pp. 116–129. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Banerjee, S., Moses, M.: Scale Invariance of Immune System Response Rates and Times: Perspectives on Immune System Architecture and Implications for Artificial Immune Systems. Swarm Intelligence 4(4), 301–318 (2010)CrossRefGoogle Scholar
  4. 4.
    Beauchemin, C., Dixit, N., Perelson, A.: Characterizing T Cell Movement within Lymph Nodes in the Absence of Antigen. The Journal of Immunology 178, 5505–5512 (2007)CrossRefGoogle Scholar
  5. 5.
    Calder, W.: Size, Function and Life History. Dover Publications, New York (1984)Google Scholar
  6. 6.
    La Gruta, N., Doherty, P.: Influenza Virology Current Topics. In: chap. Quantitative and qualitative characterization of the CD8+ T cell response to influenza virus infection. Caister Academic Press (2006)Google Scholar
  7. 7.
    Macey, R.I., Oster, G.: Berkeley Madonna, version 8.0. Tech. rep. University of California, Berkeley, California (2001)Google Scholar
  8. 8.
    Miao, H., Hollenbaugh, J., Zand, M., Holden, W., Mosmann, T.R., Perelson, A., Wu, H., Topham, D.: Quantifying the Early Immune Response and Adaptive Immune Response Kinetics in Mice Infected with Influenza A Virus. Journal of Virology 84(13), 6687–6698 (2010)CrossRefGoogle Scholar
  9. 9.
    Mitchell, H., et al.: Higher replication efficiency of 2009 (h1n1) pandemic influenza than seasonal and avian strains: kinetics from epithelial cell culture and computational modeling. Journal of Virology, JVI, 01722–10 (2010)Google Scholar
  10. 10.
    Moser, B., Loetscher, P.: Lymphocyte Traffic Control by Chemokines. Nature Immunology 2, 123–128 (2001)CrossRefGoogle Scholar
  11. 11.
    Moses, M., Banerjee, S.: Biologically Inspired Design Principles for Scalable, Robust, Adaptive, Decentralized Search and Automated Response (RADAR). In: IEEE Symposium Series in Computational Intelligence, (SSCI) (2011)Google Scholar
  12. 12.
    Paz, T., Letendre, K., Burnside, W., Fricke, G., Moses, M.: How Ants Turn Information into Food. In: IEEE Symposium Series in Computational Intelligence, (SSCI) (2011)Google Scholar
  13. 13.
    Peters, R.: The ecological implications of body size. Cambridge University Press, Cambridge (1983)CrossRefGoogle Scholar
  14. 14.
    Saenz, R., et al.: Dynamics of Influenza Virus Infection and Pathology. Journal of Virology 84(8), 3974–3983 (2010)CrossRefGoogle Scholar
  15. 15.
    Warrender, C.: CyCells (Open source software) (2003), http://sourceforge.net/projects/cycells
  16. 16.
    Warrender, C.: Modeling intercellular interactions in the peripheral immune system. Ph.D. thesis, University of New Mexico (2004)Google Scholar
  17. 17.
    Weibel, E.R.: Scaling of structural and functional variables in the respiratory system. Annual Review of Physiology 49, 147–159 (1987)CrossRefGoogle Scholar
  18. 18.
    West, G., Brown, J., Enquist, B.: A general model for the origin of allometric scaling laws in biology. Science 276(5309), 122–126 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Soumya Banerjee
    • 1
  • Drew Levin
    • 1
  • Melanie Moses
    • 1
  • Frederick Koster
    • 2
  • Stephanie Forrest
    • 1
  1. 1.Department of Computer ScienceUniversity of New MexicoUSA
  2. 2.Department of PathologyUniversity of New MexicoUSA

Personalised recommendations