Abstract
In MMAS-based biological system simulation, it is a challenging task to deal with numerous interactions among a vast number of autonomous agents. In our work, a hybrid massively multi-agent systems (MMAS) model is developed, and it incorporates the characteristics of cellular automaton (CA) and system-level mathematical equation modeling to simulate HIV-immune interaction dynamics. The mathematical equations are adopted within the site of a two-dimensional lattice. As the average high density, agent interactions can be calculated according to the equations without significantly affecting the performance of the systems studied. In the mean time, the CA model keeps the spatial characteristics of HIV evolution among the sites. The simulation based on the implemented MMAS discovers the dynamics of HIV evolution over different temporal and spatial scales, and reproduces the typical three-stage dynamics of HIV 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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Liu, J., Jin, X.L., Tsui, K.C.: Autonomy Oriented Computing (AOC): From Problem Solving to Complex Systems Modeling. Springer, Heidelberg (2004)
Newman, M.E.J.: A Model of Mass Extinction. Santa Fe Institute Working Papers (1997), http://ideas.repec.org/p/wop/safiwp/97-02-013.html
Romualdo, P.S., Alessandro, V.: Epidemic Spreading in Scale Free Networks. Physical Review Letters, 3200–3203 (2001)
Holland, J.H.: Genetic Algorithm and the Optimal Allocations of Trials. SIAM Journal of Computing 2, 88–105 (1973)
Dorigo, M., Caro, G.D.: The Ant Colony Optimization Meta-Heuristic. New Ideas in Optimization, pp. 11–32. McGraw-Hill, New York (1999)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2), 137–172 (1999)
Bernardes, A.T., Santos, R.M.: Immunization and Aging: A Learning Process in the Immune Network. Physical Review Letters 81, 3034–3037 (1998)
Santos, R.M.: Immune Responses: Getting Close to Experimental Results with Cellular Automata Models. Annual Reviews of Computational Physics VI, 159–202 (1999)
Santos, R., Coutinho, S.: On the Dynamics of the Evolution of HIV Infection (2000), http://arxiv.org/abs/cond-mat/0008081
Hershberg, U., Louzoun, Y., Atlan, H., Solomon, S.: HIV Time Hierarchy: Winning the War while. Loosing all the Battles. Physica A 289, 178–190 (2000)
Morel, P.A.: Mathematical Modeling of Immunological Reactions. Frontiers in Bioscience 3, 338–347 (1998)
Louzoun, Y., Solomon, S., Atlan, H., Cohen, I.R.: The Emergence of Spatial Complexity in the Immune System (2000), http://xxx.lanl.gov/abs/nlin.AO/0008133
Louzoun, Y., Solomon, S., Atlan, H., Cohen, I.R.: Microscopic Discrete Proliferating Components Cause the Self-organized Emergence of Macroscopic Adaptive Features in Biological Systems (2000), http://xxx.lanl.gov/abs/nlin.AO/0006043
Nowak, M.A., Bangham, C.R.M.: Population Dynamics of Immune Responses to Persistent Viruses. Science 272, 74–79 (1996)
Kirschner, D.E., Webb, G.F.: A Mathematical Model of Combined Drug Therapy of HIV Infection. Journal of Theoretical Medicine 1, 25–34 (1997)
Kirschner, D.E., Mehr, R., Perelson, A.S.: Role of the Thymus in Pediatric HIV-1 Infection. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 18, 95–109 (1998)
Kirschner, D.E.: Using Mathematics to Understand HIV Immune Dynamics. IN: Notices of the American Mathematical Society, pp. 191–202 (1996)
Liu, J., Zhang, S.W., Yang, J.: Characterizing Web usage regularities with information foraging agents. IEEE Transactions on Knowledge and Data Engineering 16(5), 566–584 (2004)
Coffin, J.M.: HIV Population Dynamics in Vivo: Implications for Genetic Variation, Pathogenesis, and Therapy. Science 267, 483–489 (1995)
Perelson, A.S., Newmann, 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)
Fauci, A.S.: The Immunodeficiency Virus: Infectivity and Mechanisms of Pathogenesis. Science 239, 617–622 (1988)
McCune, J.M.: The Dynamics of CD4+ T-cell Depletion in HIV Disease. Nature 410, 974–979 (2001)
Wei, X., et al.: Viral Dynamics in Human Immunodeficiency Virus Type 1 Infection. Nature 373, 117–122 (1995)
Pennisi, E., Cohen, J.: Eradicating HIV from a Patient: Not Just a Dream? Science 272, 1884 (1996)
Adamic, L.A., Huberman, B.A.: Technical Comment to “Emergence of Scaling in Random Networks”. Science 286(15), 509–512 (1999)
Nowak, M.A., Anderson, R.M., Boerlijst, M.C., Bonhoeffer, S., May, R.M., McMichal, A.J.: HIV-1 Evolution and Disease Progression. Science 274, 1008–1010 (1996)
Rodrigo, A.G.: HIV Evolutionary Genetics. Proceedings of the National Academy of Sciences 6, 10559–10561 (1999)
Bonhoeffer, S., Holmes, E.C., Nowak, M.A.: Causes of HIV Diversity. Nature 376, 125 (1995)
Wolinsky, S.M., et al.: Adaptive Evolution of Human Immunodeficiency Virus-Type 1 During the Natural Course of Infection. Science 272, 537–542 (1996)
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
Zhang, S., Liu, J. (2005). A Massively Multi-agent System for Discovering HIV-Immune Interaction Dynamics. In: Ishida, T., Gasser, L., Nakashima, H. (eds) Massively Multi-Agent Systems I. MMAS 2004. Lecture Notes in Computer Science(), vol 3446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11512073_12
Download citation
DOI: https://doi.org/10.1007/11512073_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26974-8
Online ISBN: 978-3-540-31889-7
eBook Packages: Computer ScienceComputer Science (R0)