Nuno Fachada
Vitor V. Lopes
ABSTRACT
Computational models of the immune system and pathogenic agents have several applications, such as theory testing and validation, or as a complement to first stages of drug trials. One possible application is the prediction of the lethality of new Influenza A strains, which are constantly created due to antigenic drift and shift. Here, we present an agent-based model of immune-influenza A dynamics, with focus on low level molecular antigen-antibody interactions, in order to study antigenic drift and shift events, and analyze the virulence of emergent strains. At this stage of the investigation, results are presented and discussed from a qualitative point of view against recent and generally recognized immunology and influenza literature.
- A. Abbas and A. Lichtman. Basic immunology: functions and disorders of the immune system. Saunders Elsevier, 2nd edition, updated edition 2006--2007 edition, 2006.Google Scholar
- P. Baccam, C. Beauchemin, C. Macken, F. Hayden, and A. Perelson. Kinetics of Influenza A Virus Infection in Humans. Journal of Virology, 80(15):7590--7599, 2006.Google ScholarCross Ref
- C. Beauchemin. Probing the effects of the well-mixed assumption on viral infection dynamics. Journal of Theoretical Biology, 242(2):464--477, 2006.Google ScholarCross Ref
- C. Beauchemin, S. Forrest, and F. Koster. Modeling Influenza Viral Dynamics in Tissue. Lecture Notes in Computer Science, 4163:23--36, 2006. Google ScholarDigital Library
- C. Beauchemin, J. McSharry, G. Drusano, J. Nguyen, G. Went, R. Ribeiro, and A. Perelson. Modeling amantadine treatment of influenza A virus in vitro. Journal of Theoretical Biology, 254(2):439--451, 2008.Google ScholarCross Ref
- C. Beauchemin, J. Samuel, and J. Tuszynski. A Simple Cellular Automaton Model for Influenza A Viral Infections. Journal of Theoretical Biology, 232(232):223--234, 2005.Google ScholarCross Ref
- M. Bernaschi and F. Castiglione. Design and implementation of an immune system simulator. Computers in Biology and Medicine, 31(5):303--331, 2001.Google ScholarCross Ref
- G. Bocharov and A. Romanyukha. Mathematical model of antiviral immune response. III. Influenza A virus infection. J Theor Biol, 167(4):323--360, 1994.Google ScholarCross Ref
- E. Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(3):7280--7287, May 2002.Google ScholarCross Ref
- F. Castiglione and M. Bernaschi. HIV-1 Strategies of Immune Evasion. International Journal of Modern Physics C, 16(12):1869--1878, 2005.Google ScholarCross Ref
- F. Celada and P. Seiden. A Computer Model of Cellular Interactions in the Immune System. Immunology Today, 13(2):56--62, 1992.Google ScholarCross Ref
- I. Cohen. Modeling Immune Behavior for Experimentalists. Immunological Reviews, 216(1):232--236, 2007.Google ScholarCross Ref
- N. Fachada. Agent-based Simulation of the Immune System. Master's thesis, Instituto Superior Técnico, Lisboa, July 2008.Google Scholar
- N. Fachada, V. V. Lopes, and A. Rosa. Simulation of Immune Response to Bacterial Challenge. In Proceedings of the 22nd annual European Simulation and Modelling Conference, pages 252--257. Eurosis, October 2008.Google Scholar
- V. Folcik, G. An, and C. Orosz. The Basic Immune Simulator: An agent-based model to study the interactions between innate and adaptive immunity. Theoretical Biology and Medical Modelling, 4(39), September 2007.Google Scholar
- S. Forrest and C. Beauchemin. Computer Immunology. Immunological Reviews, 216(1):176--197, 2007.Google ScholarCross Ref
- A. Grilo, A. Caetano, and A. Rosa. Agent based Artificial Immune System. In Proc. GECCO-01, Vol. LBP, pages 145--151, 2001.Google Scholar
- Z. Guo, H. K. Han, and J. C. Tay. Sufficiency Verification of HIV-1 Pathogenesis based on Multi-Agent Simulation. In Proceedings of the 2005 conference on Genetic and Evolutionary Computation, pages 305--312. ACM Press New York, NY, USA, 2005. Google ScholarDigital Library
- A. Hampson and J. Mackenzie. The influenza viruses. Medical Journal Australia, 185(10 Suppl):S39--S43, 2006.Google Scholar
- B. Hancioglu, D. Swigon, and G. Clermont. A dynamical model of human immune response to influenza A virus infection. Journal of Theoretical Biology, 246(1):70--86, 2007.Google ScholarCross Ref
- S. Kleinstein and P. Seiden. Simulating the Immune System. Computing in Science and Engineering, 2(4):69--77, 2000. Google ScholarDigital Library
- B. Kohler, R. Puzone, P. Seiden, and F. Celada. A systematic approach to vaccine complexity using an automaton model of the cellular and humoral immune system. i. viral characteristics and polarized responses. Vaccine, 19(7--8):862--76, 2000.Google Scholar
- Y. Louzoun. The Evolution of Mathematical Immunology. Immunological Reviews, 216(1):9--20, 2007.Google ScholarCross Ref
- M. Meier-Schellersheim and G. Mack. SIMMUNE, a tool for simulating and analyzing immune system behavior. Technical report, Institut fr Theoretische Physik, Universitt Hamburg, 1999.Google Scholar
- M. North, N. Collier, and J. Vos. Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit. ACM Transactions on Modeling and Computer Simulation, 16(1):1--25, 2006. Google ScholarDigital Library
- M. Robbins and S. Garrett. Evaluating Theories of Immunological Memory Using Large-Scale Simulations. In C. Jacob, M. L. Pilat, P. J. Bentley, and J. Timmis, editors, Artificial Immune Systems, volume 3627 of Lecture Notes in Computer Science, chapter 16, pages 193--206. Springer Berlin / Heidelberg, 2005. Google ScholarDigital Library
- I. M. Roitt and P. J. Delves. Essential Immunology. Blackwell Publishing, 10th edition, 2001.Google Scholar
- K. Ryan and C. Ray. Sherris Medical Microbiology: An Introduction to Infectious Diseases. McGraw-Hill Medical, 2004.Google Scholar
- J. Tay and A. Jhavar. CAFISS: a Complex Adaptive Framework for Immune System Simulation. In Proceedings of the 2005 ACM symposium on Applied Computing, pages 158--164. ACM Press New York, NY, USA, 2005. Google ScholarDigital Library
- R. Wagner, M. Matrosovich, and H. Klenk. Functional balance between haemagglutinin and neuraminidase in influenza virus infections. Rev. Med. Virol., 12:159--166, 2002.Google ScholarCross Ref
- C. Warrender. Modeling intercellular interactions in the peripheral immune system. PhD thesis, The University of New Mexico, 2004. Google ScholarDigital Library
- World Health Organization. http://www.who.int/.Google Scholar
- M. Zambon. Epidemiology and pathogenesis of influenza. Journal of Antimicrobial Chemotherapy, 44:3--9, 1999.Google ScholarCross Ref
Index Terms
- Simulating antigenic drift and shift in influenza A
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