A simulation framework to investigate in vitro viral infection dynamics

Virus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24 h post infection. Using a simulated annealing algorithm we tune free parameters with data from SARS-CoV infection of cultured lung epithelial cells. We also interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles.

► We present a tool for leveraging in vitro experimental data based on the cellular automaton modeling methodology that represents key stages of viral infection including initial viral infection, viral release, diffusion, and a secondary round of infection. ► We demonstrate the model's utility using data from a SARS-CoV infection of Calu-3 lung epithelial cells. ► Simulated annealing is used to identify free parameter that fit our model to SARS infection experiments performed on cultured bronchial epithelial cells. ► Latin Hypercube sampling sensitivity analysis is performed to identify key stages of viral infection. ► Results indicate that a small population of cells is initially infected and that additional rounds of infection are likely responsible for virus titer measurements.