A Simulation Framework to Investigate in vitro Viral Infection Dynamics

Area of Science:

• Virology
• Computational Biology
• Cell Biology

Background:

• In vitro viral infection studies offer controlled environments but face challenges in interpreting complex spatio-temporal dynamics.
• Understanding the intricate mechanisms of virus spread and host cell interaction is crucial for deciphering viral pathogenesis.

Purpose of the Study:

• To develop and utilize a cellular automata model for simulating in vitro viral infections, specifically focusing on SARS-CoV dynamics.
• To analyze the spatial characteristics of virus spreading and identify key mechanisms driving infection within the first 24 hours post-infection.

Main Methods:

• A cellular automata model was developed to simulate in vitro viral infections, incorporating spatial virus spreading within cell cultures.
• Latin Hypercube sensitivity analysis was employed to interrogate the model and determine the criticality of various infection mechanisms.

Main Results:

• The model successfully simulates critical aspects of in vitro viral infections, including spatial virus spreading.
• Sensitivity analysis identified key mechanisms influencing host cell infection and virus particle release during early SARS-CoV infection.

Conclusions:

• Cellular automata modeling provides a valuable tool for dissecting complex spatio-temporal processes in viral infections.
• The study highlights specific mechanisms critical to early SARS-CoV infection dynamics, aiding in a deeper understanding of viral pathogenesis.