A Simulation Framework to Investigate in vitro Viral Infection Dynamics

Area of Science:

• Virology
• Computational Biology
• Infectious Disease Modeling

Background:

• In vitro viral infection studies offer controlled environments but struggle with complex spatio-temporal dynamics.
• Understanding viral spread and host cell interaction is crucial for disease control.

Purpose of the Study:

• To develop and validate a cellular automata model for simulating in vitro SARS-CoV infection.
• To analyze the spatial dynamics of virus spreading in cultured cells.
• To identify critical mechanisms driving host cell infection and virus release within 24 hours post-infection.

Main Methods:

• A cellular automata model was developed to simulate in vitro viral infections, incorporating spatial virus spread.
• Simulated annealing was used to parameterize the model with experimental data from SARS-CoV infected lung epithelial cells.
• Latin Hypercube sensitivity analysis was employed to determine the influence of different mechanisms on infection dynamics.

Main Results:

• The model successfully simulates critical aspects of in vitro SARS-CoV infection dynamics.
• Key mechanisms influencing host cell infection and virus particle release were identified.
• Spatial characteristics of virus spreading were found to be significant factors.

Conclusions:

• The cellular automata model provides a valuable tool for dissecting complex in vitro viral infection processes.
• The study elucidates key mechanisms governing early SARS-CoV infection dynamics.
• This computational approach aids in understanding virus-host interactions and guiding experimental design.