Simulation of a stochastic cellular automata HIV/AIDS model for investigation of spatial pattern formation mediated by CD4+ T cells and HIV dynamics

  • Authors:

  • Monamorn Precharattana;Wannapong Triampo;Charin Modchang;Darapond Triampo;Yongwimon Lenbury

  • Affiliations:

  • R&D Group of Biological and Environmental Physics, Department of Physics, Faculty of Science, Mahidol University, Bangkok, Thailand;R&D Group of Biological and Environmental Physics, Dept. of Physics, Fac. of Science, Mahidol Univ., Bangkok, Thailand and Center of Excellence for Vector and Vector-Borne Diseases, Mahidol Un ...;R&D Group of Biological and Environmental Physics, Department of Physics, Faculty of Science, Mahidol University, Bangkok, Thailand and ThEP Center, Bangkok, Thailand;R&D Group of Biological and Environmental Physics, Dept. of Physics, Fac. of Science, Mahidol Univ., Bangkok, Thailand and Center of Excellence for Vector and Vector-Borne Diseases, Mahidol Un ...;Department of Mathematics, Faculty of Science, Mahidol University, Bangkok, Thailand and Center of Excellence in Mathematics, PERDO, Commission on Higher Education, Thailand

  • Venue:
  • ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
  • Year:
  • 2010

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Abstract

As infection of target immune cells by HIV mainly takes place in the lymphoid tissue, cellular automata (CA) models thus represent a significant step of understanding when the infected population is dispersed over the tissue. Motivated by these considerations, we have introduced a stochastic CA model for HIV dynamics and, particularly, explored its spatiotemporal pattern of infection. In good agreement, the model is successful to reproduce the typical evolution of HIV which is observed in the dynamics of CD4+T cells and infected CD+T cells in infected patients. The geographical result illustrates how infected cell distributions can be dispersed by spatial community. We have found that pattern formation is based on the relationship among cell states, the set of local transition rules, the conditions and the parameters in the system. The main finding is that the characteristics of dead cells barriers, which greatly control pattern formation in our system, take part in limiting the spread of infection, as well as in bringing the system dynamics toward the end phase of the time course of infection.