Modeling and Simulation of Visual Tri-Tier Immune System


Article Preview

A visual modelling approach and its computational technique were proposed to represent and simulate a kind of immune system, which is comprised of immune cells and immune molecules etc. To study natural immune system and artificial immune system according to information theories and computational methodologies, the hierarchical model of the immune system was proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system basically consisted of innate immune tier, adaptive immune tier and immune cell tier. Thus, the tri-tier model of the immune system was seamless and coherent with the architecture of the artificial immune system, so that the research on the natural immune system and the research on the artificial one could improve and synchronize each other. Though the structure and features of the natural immune system were difficult to measure and test, the tri-tier architecture and qualitative features of the artificial immune system were built, changed and verified. To validate the new approach to visualize and explore the natural immune system, many experiments were tested on the tri-tier artificial immune system. At last, the visual results of the simulations show that the visual modelling approach can provide an effective and better way of understanding the natural immune system.



Edited by:

Zhixiang Hou






T. Gong et al., "Modeling and Simulation of Visual Tri-Tier Immune System", Applied Mechanics and Materials, Vols. 48-49, pp. 701-704, 2011

Online since:

February 2011




[1] Oprea M, Forrest S, in: How the immune system generates diversity: Pathogen space coverage with random and evolved antibody libraries. Genetic & Evolutionary Computation Conference, (1999).

[2] Oprea M, Forrest S, in: Simulated evolution of antibody gene libraries under pathogen selection. IEEE International Conference on Systems, Man and Cybernetics, (1998).

DOI: 10.1109/icsmc.1998.726678

[3] Perelson A, Hightower R, Forrest S, in: Evolution (and learning) of v-region genes [J]. Research in Immunology, 1996, 147: 202-208.

DOI: 10.1016/0923-2494(96)87221-x

[4] Anthony S Fauci, in: HIV and AIDS: 20 years of science [J]. Nature Medicine, 2003, 9(7): 839-843.

[5] Balthrop J, Forrest S, Newman E J M, et al, in: Technological Networks and the Spread of Computer Viruses [J]. Science, 2004, 304 (5670): 527-529.

DOI: 10.1126/science.1095845

[6] Chao D L, Davenport M P, Forrest S, et al, in: Modelling the impact of antigen kinetics on T-cell activation and response [J]. Immunology and Cell Biology, 2004, 82(1): 55-61.

[7] Timmis J, in: aiVIS–artificial immune network visualization. Proceedings of EuroGrapgics UK 2001 Conference, (2001).

[8] Gong T. Oracle9i JDeveloper Development Introduction [M]. Beijing: China WaterPower Press, (2004).

[9] Antia R, Bergstrom T C, Pilyugin S S, et al, in: Models of CD8+ responses: 1. what is the antigen-independent proliferation program [J]. J Theor Biol, 2003, 221(4): 585–98.

[10] Bell I G, in: Mathematical model of clonal selection and antibody production [J]. J Theor Biol, 1970, 29(2): 191–232.

[11] Bocharov A G, in: Modelling the dynamics of LCMV infection in mice: conventional and exhaustive CTL responses [J]. J Theor Biol, 1998, 192(3): 283–308.

DOI: 10.1006/jtbi.1997.0612

[12] Dibrov F B, Livshits A M, Volkenstein V M, in: Mathematical model of immune processes [J]. J Theor Biol, 1977, 65(4): 609–631.

DOI: 10.1016/0022-5193(77)90012-1

[13] Ho D D, Neumann U A, Perelson S A, et al, in: Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection [J]. Nature, 1995, 373(6510): 123–126.

DOI: 10.1038/373123a0

[14] Nowak A M, Bangham R C, in: Population dynamics of immune responses to persistent viruses [J]. Science, 1996, 272(5258): 74–79.

DOI: 10.1126/science.272.5258.74

[15] Perelson S A, Weisbuch G, in: Modeling immune reactivity in secondary lymphoid organs [J]. Bull Math Biol, 1992, 54(4): 649–672.

DOI: 10.1016/s0092-8240(05)80080-1

[16] Pˇrikrylov´D a, J´ılek M, Waniewski J. Mathematical modeling of the immune response [M]. CRC Press, Boca Raton, Florida, (1992).

[17] Chao L D, Davenport P M, Forrest S, et al, in: A stochastic model of cytotoxic T cell responses [J]. Journal of Theoretical Biology, 2004, 228(2): 227-240.

DOI: 10.1016/j.jtbi.2003.12.011

[18] Gong T, in: An Immune Agent for Web-based AI Course [J]. International Journal on E-Learning, 2006, 5(4): 493-506.

In order to see related information, you need to Login.