Sego T J, Aponte-Serrano Josua O, Gianlupi Juliano Ferrari, Heaps Samuel R, Breithaupt Kira, Brusch Lutz, Crawshaw Jessica, Osborne James M, Quardokus Ellen M, Plemper Richard K, Glazier James A
Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
Biocomplexity Institute, Indiana University, Bloomington, IN, USA.
bioRxiv. 2020 Sep 26:2020.04.27.064139. doi: 10.1101/2020.04.27.064139.
Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.
对像新冠肺炎这样的原发性急性病毒感染的组织特异性效应进行模拟，对于理解疾病结局和优化治疗方法至关重要。此类模拟需要支持根据对感染机制理解的快速进展进行持续更新，以及多组人员并行开发组件。我们提出了一个用于上皮组织、病毒感染、细胞免疫反应和组织损伤的多尺度时空模拟的开源平台，该平台经过专门设计，具有模块化和可扩展性，以支持持续更新和并行开发。对简化的上皮组织片和免疫反应的基础模拟展示了从广泛感染到复发再到清除的不同感染动态模式。较慢的病毒内化和较快的免疫细胞募集减缓了感染并促进了控制。由于抗病毒药物可能有副作用，并且在感染后期给药时临床效果会降低，我们研究了感染后治疗效力和首次治疗时间对疾病进展的影响。在模拟中，即使是使用一种能大幅降低病毒RNA复制率的药物进行低效治疗，在感染开始时给药也能大大降低总组织损伤和病毒载量。剂量和治疗时间的许多组合会导致随机结果，一些模拟副本显示清除或控制（治疗成功），而另一些则显示所有上皮细胞快速感染（治疗失败）。因此，虽然高效力治疗在后期给药时通常效果较差，但后期治疗偶尔也会有效。我们展示了如何使用我们的软件模块和公开可用的软件库来扩展该平台，以模拟特定病毒类型（如丙型肝炎）并添加额外的细胞机制（组织恢复和细胞对感染的易感性变化）。
