Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
Modeling the interaction of the immune system with HIV
Mathematical and statistical approaches to AIDS epidemiology
Stochastic simulation of HIV population dynamics through complex network modelling
International Journal of Computer Mathematics - COMPLEX NETWORKS
Stochastic modeling of temporal variability of HIV-1 population
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An enhanced massively multi-agent system for discovering HIV population dynamics
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Cellular automata with fuzzy parameters in microscopic study of positive HIV individuals
Mathematical and Computer Modelling: An International Journal
AOC-by-self-discovery modeling and simulation for HIV
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
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In this study, we employ non-uniform Cellular Automata (CA) to simulate drug treatment of HIV infection, where each computational domain may contain different CA rules, in contrast to normal uniform CA models. Ordinary (or partial) differential equation models are insufficient to describe the two extreme time scales involved in HIV infection (days and decades), as well as the implicit spatial heterogeneity [4,3, 10]. R.M. Zorzenon dos Santose [13] (2001) reported a cellular automata approach to simulate three-phase patterns of human immunodeficiency virus (HIV) infection consistingof primary response, clinical latency and onset of acquired immunodeficiency syndrome (AIDS), Here we report a related model. We developed a non-uniform CA model to study the dynamics of drug therapy of HIV infection, which simulates four- phases (acute, chronic, drug treatment responds and onset of AIDS). Our results indicate that both simulations (with and without treatments) evolve to the relatively same steady state (characteristic of Wolfram's class II behaviour). Three different drugtherapies (mono-therapy, combined drug therapy and highly active antiretroviral therapy HAART) can also be simulated in our model. Our model for prediction of the temporal behaviour of the immune system to drug therapy qualitatively corresponds to clinical data.