A probability cellular automaton model for hepatitis B viral infections

Area of Science:

• Virology
• Computational Biology
• Mathematical Modeling

Background:

• Current hepatitis B virus (HBV) infection models assume uniform mixing of virus and cell populations.
• Real-world HBV infection systems exhibit spatial heterogeneity, where localized dead cells can influence infection spread.
• Existing models do not fully capture the complex spatial dynamics of HBV infection.

Purpose of the Study:

• To develop a novel 2D Cellular Automaton model for simulating HBV infection dynamics.
• To incorporate spatial factors and different HBV particle types (infectious and non-infectious) into the model.
• To investigate the role of spatial heterogeneity in HBV infection development and outcomes.

Main Methods:

• A simple 2D probability Cellular Automaton model was designed.
• The model simulates the dynamic process of HBV infection, considering spatial interactions.
• Infectious and non-infectious HBV particles were included in the simulation parameters.

Main Results:

• The Cellular Automaton model successfully reproduced key features of HBV infection, including variability in manifestation.
• The model demonstrated an ability to account for the age dependency observed in HBV infections.
• The influence of model parameters on the infection's dynamical process was systematically investigated.

Conclusions:

• The developed 2D Cellular Automaton model provides a valuable tool for understanding HBV infection dynamics.
• Spatial heterogeneity plays a significant role in the development and outcome of HBV infections.
• This modeling approach can be extended to study other complex biological systems, particularly persistent infections.