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A Cellular Automata Model on HIV Infection (2)

A Cellular Automata Model on HIV Infection (2). Shiwu Zhang Based on [Pandey et al ’s 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).

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A Cellular Automata Model on HIV Infection (2)

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  1. A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]

  2. Review: CA models on HIV(1) • Characteristics • Local interactions • Inhomogeneous elements • Spatial structure • High workload • Examples • Santos2001 • Hershberg2001

  3. Review: CA models on HIV(1) • Santos’ CA model • One type cell with 4 different states on one site • No mutation • 3-stage evolution: different time scale • Hershberg’s model in “shape space” • Virtual space, 2 types of cells • Mutation • 3-stage evolution: different time scale

  4. Pandey’s model: Introduction(1) • Elements • 2-dimension or 3-dimension lattice, • Four types of entities: • Macrophage(M) • Helper(H) • Cytotoxic cells(C) • Antigen/Viral carrier cells(V) • Entity States: • 0: low concentration • 1: high concentration

  5. Pandey’s model: Introduction(2) • Rules • Boolean expression(4) • viral mutation(10) • Fuzzy set • CA sum rules

  6. Pandey’s model: Result • Populations of Cells and virus • Initial immune response • Influence factors: • Viral mutation rate • Initial concentrations of cells • Cellular mobility

  7. 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 stimulation • Immune system’s global ability:memory • Result: • 3-stage dynamics of AIDS • HIV strain diversity • Mechanism influence

  8. 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. (2000). 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).

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