A comparative study on modeling strategies for immune system dynamics under HIV-1 infection

  • Authors:

  • Zaiyi Guo;Joc Cing Tay

  • Affiliations:

  • Evolutionary and Complex Systems Lab, Nanyang Technological University;Evolutionary and Complex Systems Lab, Nanyang Technological University

  • Venue:
  • ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
  • Year:
  • 2005

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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.