Comparing In Silico Results to In Vivo and Ex Vivo of Influenza-Specific Immune Responses After Vaccination or Infection in Humans

  • Filippo CastiglioneEmail author
  • Benjamin Ribba
  • Olivier Brass


In this chapter we describe a computer simulation system focused on the immune response. The objective of this study is to show to what extent a computational model can be used as an in silico tool to compare alternative vaccine formulations, to show strengths and weaknesses of this approach and to identify points of intervention to improve biological fidelity of the results. The model gives an example of how to conduct biomedical research by using mathematical and computational methods to evaluate hypotheses and to predict clinical outcomes. Specifically, we show that prime-boost vaccination protocols can be ­modeled and used to elucidate the protective role of the immune memory elicited by priming with either influenza vaccines or influenza infection.


H1N1 Computer simulation Prime-boost vaccination S-OIV Epitope prediction 



The authors are thankful to Frederic Vogel for insightful comments and suggestions.


  1. Bernaschi M, Castiglione F (2001) Design and implementation of an immune system simulator. Comp Biol Med 31:303–331CrossRefGoogle Scholar
  2. Bidot C, Gruy F, Haudin CS, El Hentati F, Guy B, Lambert C (2008) Mathematical modeling of T-cell activation kinetic. J Comput Biol 15:105–128PubMedCrossRefGoogle Scholar
  3. Brenner S, Milstein C (1966) Origin of antibody variation. Nature 211:242–243PubMedCrossRefGoogle Scholar
  4. Burnet FM (1959) The clonal selection theory of acquired immunity. Vanderbuil University press, NashvilleGoogle Scholar
  5. Castiglione F (2006) Agent based modeling. Scholarpedia 1:1562CrossRefGoogle Scholar
  6. Castiglione F (2009) Agent based modeling and simulation, introduction to. In: Meyers R (ed) Encyclopedia of complexity and systems science, vol 1. Springer, New YorkGoogle Scholar
  7. Castiglione F, Santoni D, Rapin N (2011) CTLs’ Repertoire shaping in the thymus: a Montecarlo simulation. Autoimmunity 44:1–10CrossRefGoogle Scholar
  8. Cox RJ, Brokstad KA, Zuckerman MA, Wood JM, Haaheim LR, Oxford JS (1994) An early humoral immune response in peripheral blood following parenteral inactivated influenza vaccination. Vaccine 12:993–999PubMedCrossRefGoogle Scholar
  9. El-Madhun AS, Cox RJ, Søreide A, Olofsson J, Haaheim LR (1998) Systemic and mucosal immune responses in young children and adults after parenteral influenza vaccination. J Infect Dis 178(4):933–939PubMedCrossRefGoogle Scholar
  10. El-Madhun AS, Cox RJ, Haaheim LR (1999) The effect of age and natural priming on the IgG and IgA subclass responses after parenteral influenza vaccination. J Infect Dis 180:1356–1360PubMedCrossRefGoogle Scholar
  11. Francis K, Palsson BO (1997) Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion. Proc Natl Acad Sci USA 94:12258–12262PubMedCrossRefGoogle Scholar
  12. Goldsby RA, Kindt TJ, Kuby J, Osborne BA (2000) Kuby immunology, 4th edn. W.H. Freeman & Company, New YorkGoogle Scholar
  13. Hayflick L, Moorhead PS (1961) The serial cultivation of human diploid cell strains. Exp Cell Res 25:585–621PubMedCrossRefGoogle Scholar
  14. Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol 125C:373–389Google Scholar
  15. Kaplan HS (1980) Hodgkin’s disease, 2nd edn. Harvard University Press, Cambridge, MAGoogle Scholar
  16. Lavielle M, Samson A, Karina Fermin A, Mentré F (2011) Maximum likelihood estimation of long-term HIV dynamic models and antiviral response. Biometrics 67:250–259PubMedCrossRefGoogle Scholar
  17. Lederberg J (1959) Genes and antibodies. Science 129:1649–1653PubMedCrossRefGoogle Scholar
  18. Matzinger P (1994) Tolerance, danger, and the extended family. Annu Rev Immunol 12:991–1045PubMedCrossRefGoogle Scholar
  19. Miller MJ, Wei SH, Cahalan MD, Parker I (2004) T cell repertoire scanning is promoted by dynamic dendritic cell behavior and random T cell motility in the lymph node. Proc Natl Acad Sci USA 101:998–1003PubMedCrossRefGoogle Scholar
  20. Miyazawa S, Jernigan RL (2000) Identifying sequence-structure pairs undetected by sequence alignments. Protein Eng 13:459–475PubMedCrossRefGoogle Scholar
  21. Murphy K, Travers P, Walport M (2007) Janeway’s immunobiology, 7th edn. Garland Science, New York/LondonGoogle Scholar
  22. Murphy K, Travers P, Janeway C, Walport M (2008) Janeway’s immunology. Garland Science/Taylor & Francis, New YorkGoogle Scholar
  23. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, Khan N, Li G-M, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao K, Ahmed R, Pulendran B (2011) Systems biology of vaccination for seasonal influenza in humans. Nat Immunol 12:786–795PubMedCrossRefGoogle Scholar
  24. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017PubMedCrossRefGoogle Scholar
  25. Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O (2004) Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20:1388–1397PubMedCrossRefGoogle Scholar
  26. Nossal GJV, Pike Beverley L (1980) Clonal anergy: persistence in tolerant mice of antigen-binding B lymphocytes incapable of responding to antigen or mitogen. Proc Natl Acad Sci USA 77:1602–1606PubMedCrossRefGoogle Scholar
  27. Nowak MA, Bangham CR (1996) Population dynamics of immune responses to persistent viruses. Science 272:74–79PubMedCrossRefGoogle Scholar
  28. Nowak MA, May RM (2000) Virus dynamics: mathematical principles of immunology and virology. Oxford University Press, Oxford, XIIGoogle Scholar
  29. Parker JM, Guo D, Hodges RS (1986) New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25:5425–5432PubMedCrossRefGoogle Scholar
  30. Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219–1268CrossRefGoogle Scholar
  31. Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD (1996) HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271:1582–1586PubMedCrossRefGoogle Scholar
  32. Perelson AS, Essunger P, Cao Y, Vesanen M, Hurley A et al (1997) Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387:188–191PubMedCrossRefGoogle Scholar
  33. Rapin N, Lund O, Bernaschi M, Castiglione F (2010) Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 5:e9862PubMedCrossRefGoogle Scholar
  34. Santoni D, Pedicini M, Castiglione F (2008) Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hyper-sensitivity reactions. Bioinformatics 24:1374–1380PubMedCrossRefGoogle Scholar
  35. Schanen BC, De Groot AS, Moise L, Ardito M, McClaine E, Martin W, Wittman V, Warren WL, Drake DR 3rd (2011) Coupling sensitive in vitro and in silico techniques to assess cross-reactive CD4(+) T cells against the swine-origin H1N1 influenza virus. Vaccine 29:3299–3309PubMedCrossRefGoogle Scholar
  36. Schwartz RH (2003) T cell anergy. Annu Rev Immunol 21:305–334PubMedCrossRefGoogle Scholar
  37. Segovia-Juarez JL, Ganguli S, Kirschner D (2004) Identifying control mechanisms of granuloma formation during M. Tuberculosis infection using an agent-based model. J Theor Biol 231:357–376PubMedCrossRefGoogle Scholar
  38. Stafford MA, Corey L, Cao Y, Daar ES, Ho DD et al (2000) Modeling plasma virus concentration during primary HIV infection. J Theor Biol 203:285–301PubMedCrossRefGoogle Scholar
  39. Stray SJ, Air GM (2001) Apoptosis by influenza viruses correlates with efficiency of viral mRNA synthesis. Virus Res 77:3–17PubMedCrossRefGoogle Scholar
  40. Wodarz D, Nowak MA (2002) Mathematical models of HIV pathogenesis and treatment. Bioessays 24:1178–1187PubMedCrossRefGoogle Scholar
  41. Wolfram S (2002) A New kind of science. Wolfram Media, ChampainGoogle Scholar
  42. Zhang X, Mosser DM (2008) Macrophage activation by endogenous danger signals. J Pathol 214:161–171PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Filippo Castiglione
    • 1
    Email author
  • Benjamin Ribba
    • 2
  • Olivier Brass
    • 3
  1. 1.Istituto per le Applicazioni del CalcoloNational Research Council of ItalyRomeItaly
  2. 2.INRIA, project-team NUMEDEcole Normale Supérieure de LyonLyonFrance
  3. 3.DiscoverySanofi PasteurMarcy-l’EtoileFrance

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