1 / 8

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).

aackley
Télécharger la présentation

A Cellular Automata Model on HIV Infection (2)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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).

More Related