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.

病毒感染是一种复杂的生物现象，体外实验提供了一种独特而简洁的观点，其中数据通常是在受控环境条件下从单个细胞群中获得的。尽管如此，对病毒动力学的数据解释和真正理解仍然受到各种相互交织的时空过程的绝对复杂性的阻碍。在本文中，我们提出了一种解决这些问题的工具：描述体外病毒感染的关键方面的细胞自动机模型，同时考虑了病毒在培养井内传播的空间特征。该模型的目的是了解感染后 24 小时内 SARS-CoV 感染动态的关键机制。使用模拟退火算法，我们根据培养肺上皮细胞的 SARS-CoV 感染数据调整自由参数。我们还使用拉丁超立方敏感性分析来询问模型，以确定哪些机制对于观察到的宿主细胞感染和测量到的病毒颗粒的释放至关重要。