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Stochastic modeling for dynamics of HIV-1 infection using cellular automata: A review

Monamorn Precharattana1

  • 11 Institute for Innovative Learning, Mahidol University, 999 Salaya, Phutthamonthon District Nakhon Pathom 73170, Thailand.

Journal of Bioinformatics and Computational Biology
|December 2, 2015
PubMed

Insights

Discrete models, specifically cellular automata (CA), are crucial for understanding human immunodeficiency virus type 1 (HIV-1) infection dynamics within lymphoid tissues. This research reviews CA applications for studying HIV-1 spread and identifies future research directions.

Area of Science:

  • Immunology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Discrete models are increasingly important for studying immune responses, particularly in human immunodeficiency virus type 1 (HIV-1) infection leading to AIDS.
  • HIV-1 infection primarily targets immune cells within lymphoid tissues, making spatial modeling approaches significant.

Purpose of the Study:

  • To review the development and application of cellular automata (CA) models for understanding HIV-1 infection dynamics.
  • To identify previously studied issues and future research objectives in CA-based HIV-1 dynamics.

Main Methods:

  • Review of existing literature on cellular automata (CA) models applied to HIV-1 infection.
  • Analysis of how CA have been developed and utilized to simulate HIV-1 dynamics.

Main Results:

  • Cellular automata models offer a significant approach to understanding the spatial dispersion of infected cell populations in HIV-1 infection.
  • The review highlights established research areas and delineates open questions for future investigation.

Conclusions:

  • Cellular automata provide a valuable framework for dissecting the complex spatial dynamics of HIV-1 infection within lymphoid tissues.
  • Further research using CA models is needed to address current knowledge gaps in HIV-1 pathogenesis and control.

Keywords:
Cellular automatondiscrete modelinfectious disease

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