A Cellular Automata Model on HIV Infection (2) - PowerPoint PPT Presentation

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A Cellular Automata Model on HIV Infection (2)

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Antigen/Viral carrier cells(V) Entity States: 0: low ... Viral mutation rate. Initial concentrations of cells. Cellular mobility. Comparison: our model ... – PowerPoint PPT presentation

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Title: A Cellular Automata Model on HIV Infection (2)


1
A Cellular Automata Model on HIV Infection (2)
  • Shiwu Zhang
  • Based on Pandey et als 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
  • Hershbergs model in shape space
  • Virtual space, 2 types of cells
  • Mutation
  • 3-stage evolution different time scale

4
Pandeys 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
Pandeys model Introduction(2)
  • Rules
  • Boolean expression(4)
  • viral mutation(10)
  • Fuzzy set
  • CA sum rules

6
Pandeys 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-gt 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 systems global abilitymemory
  • 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 A251
    (430).
  • R.B. Pandey et al. (2000). Effect of Cellular
    Mobility on Immune Response. Physica A283 (447).
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