A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]
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Transcript of A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]
![Page 1: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/1.jpg)
A Cellular Automata Model on HIV Infection (2)
Shiwu Zhang
Based on [Pandey et al’s work]
![Page 2: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/2.jpg)
Review: CA models on HIV(1)
• Characteristics– Local interactions
– Inhomogeneous elements
– Spatial structure
– High workload
• Examples– Santos2001
– Hershberg2001
![Page 3: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/3.jpg)
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
![Page 4: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/4.jpg)
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
![Page 5: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/5.jpg)
Pandey’s model: Introduction(2)• Rules
– Boolean expression(4)
– viral mutation(10)
– Fuzzy set
– CA sum rules
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Pandey’s model: Result
• Populations of Cells and virus– Initial immune response
• Influence factors:– Viral mutation rate– Initial concentrations of cells– Cellular mobility
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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
![Page 8: A Cellular Automata Model on HIV Infection (2) Shiwu Zhang Based on [Pandey et al’s work]](https://reader036.fdocuments.us/reader036/viewer/2022082821/5697c00b1a28abf838cc84e1/html5/thumbnails/8.jpg)
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).