Modeling the Kinetics of the Immune Response

  • Ami RadunskayaEmail author
  • Sarah Hook
Part of the SIMAI Springer Series book series (SEMA SIMAI)


In this chapter we develop a mathematical model of the immune response to a weak antigen, suitable for modeling a cancer vaccine. The parameters are calibrated to a murine model and the model is validated by comparing simulations to experimental results. The model is then used to develop a dosing strategy that optimizes the immune response.


Memory Cell Cancer Vaccine Dose Strategy Genetic Algorithm Algorithm Dose Function 
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.


  1. 1.
    Arnon, R., Ben-Yedidia, T.: Old and new vaccine approaches. Int. Immunopharmacol. 3, 1195-1204(2003)CrossRefGoogle Scholar
  2. 2.
    Barnden, M., Allison, J., Heath, W., Carbone, F.: Defective tcr expression in transgenic mice constructed using cdna-based-α- and β -chain genes under the control of heterologous regulatory elements. Immunol. Cell Biol. 76, 34–40 (1998)CrossRefGoogle Scholar
  3. 3.
    Carbone, F., Bevan, M.: Induction of ovalbumin-specific cytotoxic t cells by in vivo peptide immunization. J. Exp. Med. 169, 603–612 (1989)CrossRefGoogle Scholar
  4. 4.
    De Boer, R.J., Homann, D., Perelson, A.S.: Different dynamics of CD4+ and CD8+ T cell responses during and after acute lymphocytic choriomeningitis virus. J. Immunol. 171, 3928-3935 (2003)CrossRefGoogle Scholar
  5. 5.
    van Faassen, H., Saldanha, M., Gilbertson, D., Dudani, R., Krishnan, L., Sad, S.: Reducing the stimulation of CD8+ T cells during infection with intracellular bacteria promotes differentiation primarily into a central (CD62Lhigh CD44high) subset. J. Immunol. 174, 5341–5350 (2005)CrossRefGoogle Scholar
  6. 6.
    Homann, D., Teyton, L., Oldstone, M.: Differential regulation of anti-viral T cell immunity results in stable CD8+ but declining CCD4+ T cell memory. Nature Med. 7, 913–919 (2001)CrossRefGoogle Scholar
  7. 7.
    Keilholz, U., Martus, P., Scheibenbogen, C.: Immune monitoring of t-cell responses in cancer vaccine development. Clin. Cancer Res. 12, 2346–2352 (2006)CrossRefGoogle Scholar
  8. 8.
    Liu, M.: Vaccine developments. Nature Medicine 4, 515–519 (1998)CrossRefGoogle Scholar
  9. 9.
    Ludweig, B., Krebs, P., Junt, T., Metters, H., Ford, N.J., Anderson, R.M., Bocharov, G.: Determining control parameters for dendritic cell-cytotoxic T lymphocyte interaction. Eur. J. Immunol. 34, 2407–2418 (2004)CrossRefGoogle Scholar
  10. 10.
    Makela, P.: Vaccines, coming of age after 200 years. FEMS Microbiol. Rev. 24, 9–20 (2000)CrossRefGoogle Scholar
  11. 11.
    Melief, C., Van Der Burg, S., Toes, R., Ossendorp, F., Offringa, R.: Effective therapeutic anticancer vaccines based on precision guiding of cytolytic t lymphocytes. Immunol. Rev. 188, 177-182(2002)CrossRefGoogle Scholar
  12. 12.
    Rosenberg, S., Yang, J., Restifo, N.: Cancer immunotherapy: moving beyond current vaccines. Nature Med. 10, 909–915 (2004)CrossRefGoogle Scholar
  13. 13.
    Shiina, M., Rehermann, B.: Hepatitis c vaccines: Inducing and challenging memory t cells. Hepatology 43, 1395–1398 (2006)CrossRefGoogle Scholar
  14. 14.
    Sprent, J., Surh, C.D.: T cell memory. Annu. Rev. Immunol. 20, 551–579 (2002)CrossRefGoogle Scholar
  15. 15.
    Srivastava, P.: Therapeutic cancer vaccines. Curr. Opin. Immunol. 18, 201–205 (2006)CrossRefGoogle Scholar
  16. 16.
    Villasana, M., Ochoa, G.: Heuristic design of cancer chemotherapies. IEEE Trans. Evolutionary Comput. 8, 513-521(2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia 2012

Authors and Affiliations

  1. 1.Pomona CollegeClaremontUSA
  2. 2.Otago UniversitySchool of PharmacyDunedinNew Zealand

Personalised recommendations