Reproducibility and usability of chronic virus infection model using agent-based simulation; comparing with a...

Area of Science:

• Computational Biology
• Virology
• Epidemiology

Background:

• Chronic viral infections pose significant public health challenges.
• Mathematical models offer insights but have limitations in capturing complex biological dynamics.
• Agent-based modeling (ABM) provides a powerful alternative for simulating biological systems.

Purpose of the Study:

• To develop and validate agent-based models for simulating chronic viral infections.
• To compare simulation outcomes with mathematical models and in vivo biological observations.
• To investigate the impact of various biological parameters on viral dynamics.

Main Methods:

• Developed two distinct agent-based models using specialized software.
• Ensured model consistency through identical parameterization.
• Simulated chronic viral infection dynamics under varying parameter conditions.

Main Results:

• Simulation results exhibited a transient and an equilibrium phase with smooth transitions, aligning with in vivo biology.
• Increased virus lifespan, cell lifespan, regeneration rate, virus production, and infection rate positively correlated with equilibrium viral load.
• Extended latent period, increased infected cell lifespan reduction, and faster cell cycle negatively impacted viral load.
• Initial viral load influenced infection rate but not equilibrium conditions.
• Space size did not affect equilibrium, but peak viral load occurred at specific virus mobility relative to space size.

Conclusions:

• Agent-based models offer a reproducible visual representation of chronic viral infections.
• These models can incorporate parameters challenging for traditional mathematical approaches.
• The study provides a framework for understanding complex viral dynamics and informing therapeutic strategies.