1、A Cellular Automata Model on HIV Infection (2),Shiwu ZhangBased on Pandey et als work,Review: CA models on HIV(1),Characteristics Local interactions Inhomogeneous elements Spatial structure High workload Examples Santos2001 Hershberg2001,Review: CA models on HIV(1),Santos CA model One type cell with
2、 4 different states on one site No mutation 3-stage evolution: different time scale Hershbergs model in “shape space” Virtual space, 2 types of cells Mutation 3-stage evolution: different time scale,Pandeys model: Introduction(1),Elements 2-dimension or 3-dimension lattice, Four types of entities: M
3、acrophage(M) Helper(H) Cytotoxic cells(C) Antigen/Viral carrier cells(V) Entity States: 0: low concentration 1: high concentration,Pandeys model: Introduction(2),Rules Boolean expression(4) viral mutation(10) Fuzzy set CA sum rules,Pandeys model: Result,Populations of Cells and virus Initial immune
4、response Influence factors: Viral mutation rate Initial concentrations of cells Cellular mobility,Comparison: our model,Method: Reasonable- Convincing Multi-type elements: T cells, B cells, HIV Spatial space& shape space Accounting for important interactions HIV high mutation rate Immune cells stimu
5、lation Immune systems global ability:memory Result: 3-stage dynamics of AIDS HIV strain diversity Mechanism influence,Related Papers,R.B. Pandey. (1998). A stochastic cellular automata approach to cellular dynamics for HIV: effect of viral mutation. Theory in Bioscience: 117(32) H. Mannion et al. (2
6、000). Effect of Mutation on Helper T-cells and Viral Population: A Computer Simulation Model for HIV. Theory in Bioscience: 119(10) H. Mannion et al. (2000). A Monte Carlo Approach to Population Dynamics of Cell in an HIV Immune Response Model. Theory in Bioscience: 119(94) A. Mielke and R.B. Pandey. (1998). A computer simulation study of cell population in a fuzzy interaction model for mutating HIV. Physica A:251 (430). R.B. Pandey et al. (2000). Effect of Cellular Mobility on Immune Response. Physica A:283 (447).,
