Abstract
Considerable research effort has provided mathematical and computational models of the human immune response under viral infection. However, the quality of simulated results are highly dependent on the choice of modeling strategy. We examine two modeling approaches of HIV pathogenesis: Mathematical and Multi-Agent (or MA) Models. The latter has relatively wider Model Scope due to the agent-rule specification method. Mathematical Models employ Parameter and Population/Subpopulation Level entity granularities with equation-based interaction, while MA Models specify entities at Individual Level, implemented with agents to describe interactions via IF-THEN rules. Compared to the former, MA Models naturally handles entity heterogeneity and spatial non-uniformity, and suffers less from the issue of directly designed dynamics. Both approaches are however, not directly accessible to immunologists due to the need for programming knowledge; hence, closer collaboration between computer scientists and immunologists is necessary.
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
Preview
Unable to display preview. Download preview PDF.
References
RePast, http://repast.sourceforge.net
Banchereau, J.: The long arm of the immune system. Scientific American 287, 52–59 (2002)
Bar-Yam, Y.: Dynamics of Complex Systems. Westview Press (1997)
Bergmann, C., Van Hemmen, J.L., Segel, L.A.: Th1 or Th2: how an appropriate T helper response can be made. Bull. Math. Biol. 63, 405–430 (2001)
Caetano, A., Grilo, A.: Modeling thymic selection and concomitant immune responses on CD4+T lymphocyte sub-populations. In: Proc. 2nd workshop on BEMC (1996)
Callard, R., George, A.J., Stark, J.: Cytokines, chaos, and complexity. Immunity 11, 507–513 (1999)
Coffin, J.M.: HIV population dynamics in vivo: implications for genetic variation, pathogensis, and therapy. Science 267, 483–489 (1995)
De Boer, R., Perelson, A.S.: Target cell limited and immune control models of HIV infection: a comparison. J. Theor. Biol. 190, 201–214 (1998)
De Boer, R.J., Perelson, A.S.: T cell repertoires and competitive exclusion. J. Theor. Biol. 169, 375–390 (1994)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
Essunger, P., Perelson, A.S.: Modeling HIV infection of CD4+ T-cell subpopulations. J. Theor. Biol. 179, 367–391 (1994)
Fauci, A.S., Chun, T.-W.: Latent reservoirs of HIV: obstacles to the eradication of virus. Proc. Natl. Acad. Sci. USA 96, 10958–10961 (1999)
Gougeon, M.-L.: Apoptosis as an HIV strategy to escape immune attack. Nature Reviews Immunology 3, 392–404 (2003)
Grilo, A., Caetano, A., Rosa, A.: Immune System Simulation through a Complex Adaptive System Model. In: Proceeding of the 3rd Workshop on Genetic Algorithms and Artificial Life (1999)
Guo, Z., Han, H.K., Tay, J.C.: Sufficiency Verification of HIV-1 Pathogenesis based on Multi-Agent Simulation. In: GECCO 2005, Washington D.C. (2005)
Heinkelein, M., Sopper, S., Jassoy, C.: Contact of human immunodeficiency virus type 1-infected and uninfected CD4+ T lymphocytes is highly cytolytic for both cells. Journal of Virology 69, 6925–6931 (1995)
Hershberg, U., Louzoun, Y., Atlan, H., Solomon, S.: HIV time hierarchy: winning the war while, loosing all the battles. Physica A 289, 178–190 (2001)
Ho, D.D., Neumann, A.U., Perelson, A.S., Chen, W., Leonard, J.M., Markowitz, M.: Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373, 123–126 (1995)
Jacob, C., Litorco, J., Lee, L.: Immunity Through Swarms: Agent-Based Simulations of the Human Immmune System. In: 3rd International Conference on Artificial Immune Systems (2004)
Janeway, C.A., Travers, P., Walport, M., Shlomchik, M.: Immunobiology: The immune system in health & disease. Garland Science Publishing (2001)
Johnson, G.B.: The Living World, 2nd edn. Mc Graw Hill, New York (2000)
Klein, J.: BREVE: a 3D simulation enviornment for the simulation of decentralized systems and artificial life. In: Proceedings of ARtificial Life VIII, the 8th International Conference on the Simulation and Synthesis of Living Systems (2002)
Lagreca, M.C., de Almeida, R.M.C., Zorzenon dos Santos, R.M.: A dynamical model for the immune repertoire. Physica A 289, 191–207 (2001)
Louzoun, Y., Solomon, S., Atlan, H., Cohen, I.R.: Modeling complexity in biology. Physica A 297, 242–252 (2001)
McCune, J.M.: The dynamics of CD4 + T-cell depletion in HIV disease. Nature 410, 974–979 (2001)
Nowak, M.A., McMichael, A.J.: How HIV defeats the immune system. Scientific American 273, 58–65 (1995)
Nowak, M.A., Anderson, R.M., McLean, A.R., Wolfs, T.F.W., Goudsmit, J., May, R.M.: Antigenic diversity thresholds and the development of AIDS. Science 254, 963–969 (1991)
Nowak, M.A., May, R.M., Anderson, R.M.: The evolutionary dynamics of HIV-1 quasispecies and the development of immunodeficiency disease. AIDS 4, 1095–1103 (1990)
Perelson, A.S.: Immunology for physicists. Reviews of Modern Physics 69, 1219–1267 (1997)
Perelson, A.S.: Modelling viral and immune system dynamics. Nature Reviews Immunology 2, 28–36 (2002)
Perelson, A.S., Essunger, P., Cao, Y., Vesanen, M., Hurly, A., Saksela, K., Markowitz, M., Ho, D.D.: Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387, 188–191 (1997)
Perelson, A.S., Nelson, P.W.: Mathematical Analysis of HIV-1 Dynamics in Vivo. SIAM Review 41, 3–44 (1999)
Perelson, A.S., Neumann, A.U., Markowitz, M., Leonard, J.M., Ho, D.D.: HIV-1 Dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582–1586 (1996)
Perelson, A.S., Oster, G.F.: Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. J. Theor. Biol. 81, 645–670 (1979)
Phillips, A.N.: Reduction of HIV concentration during acute infection: independence from a specific immune response. Science 271, 497–499 (1996)
Rowland-Jones, S.L.: AIDS pathogenesis: what have two decades of HIV research taught us? Nature Reviews Immunology 3, 343–348 (2003)
Selliah, N., Finkel, T.H.: Biochemical mechanisms of HIV induced T cell apoptosis. Cell Death and Differentiation 8, 127–136 (2001)
Sieburg, H.B., McCutchan, J.A., Clay, O.K., Cabalerro, L., Ostlund, J.J.: Simulation of HIV infection in artificial immune systems. Physica D 45, 208–227 (1990)
Stafford, M.A., Corey, L., Cao, Y., Daar, E.S., Ho, D.D., Perelson, A.S.: Modeling plasma virus concentration during primary HIV infection. J. Theor. Biol. 203, 285–301 (2000)
STELLA software. High Performance Systems, Inc. Home Page, http://www.hps-inc.com
Stine, G.J.: AIDS Update. Prentice-Hall, Englewood Cliffs (1999)
Vensim. Ventana Systems. Ventana Systems Home Page, http://www.vensim.com
Wei, X., Ghosh, S.K., Taylor, M.E., Johnson, V.A., Emini, E.A., Deutsch, P., Lifson, J.D., Bonhoeffer, S., Nowak, M.A., Hahn, B.H., Saag, M.S., Shaw, G.M.: Viral dynamics in human immunodeficiency virus type 1 infection. Nature 373, 117–122 (1995)
Wein, L.M., D’Amato, R.M., Perelson, A.S.: Mathematical analysis of antiretroviral therapy aimed at HIV-1 eradication or maintenance of low viral loads. J. Theor. Biol. 192, 81–98 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Z., Tay, J.C. (2005). A Comparative Study on Modeling Strategies for Immune System Dynamics Under HIV-1 Infection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_17
Download citation
DOI: https://doi.org/10.1007/11536444_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
Online ISBN: 978-3-540-31875-0
eBook Packages: Computer ScienceComputer Science (R0)