An enhanced massively multi-agent system for discovering HIV population dynamics

Authors:
Shiwu Zhang;Jie Yang;Yuehua Wu;Jiming Liu
Affiliations:
University of Science and Technology of China, Anhui, China;University of Science and Technology of China, Anhui, China;University of Science and Technology of China, Anhui, China;Hong Kong Baptist University, Kowloon Tong, Hong Kong
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Year:
2005

Citing 2
Cited 1

Cellular Automata Model of Drug Therapy for HIV Infection

ACRI '01 Proceedings of the 5th International Conference on Cellular Automata for Research and Industry
A massively multi-agent system for discovering HIV-Immune interaction dynamics

MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems

Multi-agent model of hepatitis C virus infection

Artificial Intelligence in Medicine

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Abstract

In this paper, we present an enhanced massively multi-agent system based on the previous MMAS for discovering the unique dynamics of HIV infection [1]. The enhanced MMAS keeps the spacial characteristics of cellular automata (CA), and employs mathematical equations within sites. Furthermore, new features are incorporated into the model, such as the sequence representation of HIV genome, immune memory and agent remote diffusion among sites. The enhanced model is closer to the reality and the simulation captures two extreme time scales in the typical three stages dynamics of HIV infection, which make the model more convincing. The simulation also reveals two phase-transitions in the dynamics of the size of immune memory, and indicates that the high mutation rate of HIV is the fatal factor with which HIV destroys the immune system eventually. The enhanced MMAS provides a good tool to study HIV drug therapy for its characterizing the process of HIV infection.