Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A cellular automaton is a discrete dynamic system composed of “cells” located on a regular spatial lattice. A cell has any one of a finite number of states, and it is updated at discrete time intervals based on its prior state and the prior states of its near neighbors. All cells on the lattice are updated synchronously so that the state of the entire system advances in discrete time steps.
- 2.
On a multi-core processor the CPU time decreases almost linearly with the number of cores since the simulations are independently run in parallel on each CPU core.
- 3.
Note that the two different concentrations generally do not peak together. Thus, measurement of peak values differs from measuring concentrations at a discreet moment in time.
- 4.
The simulator implements a simple rule for excluding short amino acid fragments (less than 24 amino acids) to be searched for B cell epitopes.
References
Bernaschi M, Castiglione F (2001) Design and implementation of an immune system simulator. Comp Biol Med 31:303–331
Bidot C, Gruy F, Haudin CS, El Hentati F, Guy B, Lambert C (2008) Mathematical modeling of T-cell activation kinetic. J Comput Biol 15:105–128
Brenner S, Milstein C (1966) Origin of antibody variation. Nature 211:242–243
Burnet FM (1959) The clonal selection theory of acquired immunity. Vanderbuil University press, Nashville
Castiglione F (2006) Agent based modeling. Scholarpedia 1:1562
Castiglione F (2009) Agent based modeling and simulation, introduction to. In: Meyers R (ed) Encyclopedia of complexity and systems science, vol 1. Springer, New York
Castiglione F, Santoni D, Rapin N (2011) CTLs’ Repertoire shaping in the thymus: a Montecarlo simulation. Autoimmunity 44:1–10
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 12:993–999
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):933–939
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 180:1356–1360
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 94:12258–12262
Goldsby RA, Kindt TJ, Kuby J, Osborne BA (2000) Kuby immunology, 4th edn. W.H. Freeman & Company, New York
Hayflick L, Moorhead PS (1961) The serial cultivation of human diploid cell strains. Exp Cell Res 25:585–621
Jerne NK (1974) Towards a network theory of the immune system. Ann Immunol 125C:373–389
Kaplan HS (1980) Hodgkin’s disease, 2nd edn. Harvard University Press, Cambridge, MA
Lavielle M, Samson A, Karina Fermin A, Mentré F (2011) Maximum likelihood estimation of long-term HIV dynamic models and antiviral response. Biometrics 67:250–259
Lederberg J (1959) Genes and antibodies. Science 129:1649–1653
Matzinger P (1994) Tolerance, danger, and the extended family. Annu Rev Immunol 12:991–1045
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 101:998–1003
Miyazawa S, Jernigan RL (2000) Identifying sequence-structure pairs undetected by sequence alignments. Protein Eng 13:459–475
Murphy K, Travers P, Walport M (2007) Janeway’s immunobiology, 7th edn. Garland Science, New York/London
Murphy K, Travers P, Janeway C, Walport M (2008) Janeway’s immunology. Garland Science/Taylor & Francis, New York
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 12:786–795
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 12:1007–1017
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 20:1388–1397
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 77:1602–1606
Nowak MA, Bangham CR (1996) Population dynamics of immune responses to persistent viruses. Science 272:74–79
Nowak MA, May RM (2000) Virus dynamics: mathematical principles of immunology and virology. Oxford University Press, Oxford, XII
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 25:5425–5432
Perelson AS, Weisbuch G (1997) Immunology for physicists. Rev Mod Phys 69:1219–1268
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 271:1582–1586
Perelson AS, Essunger P, Cao Y, Vesanen M, Hurley A et al (1997) Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387:188–191
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:e9862
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 24:1374–1380
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 29:3299–3309
Schwartz RH (2003) T cell anergy. Annu Rev Immunol 21:305–334
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 231:357–376
Stafford MA, Corey L, Cao Y, Daar ES, Ho DD et al (2000) Modeling plasma virus concentration during primary HIV infection. J Theor Biol 203:285–301
Stray SJ, Air GM (2001) Apoptosis by influenza viruses correlates with efficiency of viral mRNA synthesis. Virus Res 77:3–17
Wodarz D, Nowak MA (2002) Mathematical models of HIV pathogenesis and treatment. Bioessays 24:1178–1187
Wolfram S (2002) A New kind of science. Wolfram Media, Champain
Zhang X, Mosser DM (2008) Macrophage activation by endogenous danger signals. J Pathol 214:161–171
Acknowledgments
The authors are thankful to Frederic Vogel for insightful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Castiglione, F., Ribba, B., Brass, O. (2012). Comparing In Silico Results to In Vivo and Ex Vivo of Influenza-Specific Immune Responses After Vaccination or Infection in Humans. In: Baschieri, S. (eds) Innovation in Vaccinology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4543-8_2
Download citation
DOI: https://doi.org/10.1007/978-94-007-4543-8_2
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4542-1
Online ISBN: 978-94-007-4543-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)