Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA 87545.
Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK.
PLoS Comput Biol. 2020 Nov 2;16(11):e1008375. doi: 10.1371/journal.pcbi.1008375. eCollection 2020 Nov.
Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.
数学建模已成功用于对许多病毒感染进行定量描述，但对于需要进行生物安全 4 级实验的埃博拉病毒，建模可以发挥关键作用。埃博拉病毒建模工作主要集中在体内病毒动力学上，例如在动物模型中，以帮助开发抗病毒药物和疫苗。但是，到目前为止，这些研究尚未详细说明感染周期，而这可以提供对病毒动力学的基本描述，从而更深入地了解其临床表现。在这里，我们获得了埃博拉病毒体外感染的一组多样化的实验数据集，然后利用贝叶斯推断方法来完全确定感染模型中的参数。我们的研究结果提供了有关受感染细胞处于潜伏期（感染和开始产生病毒之间的时间段）的时间分布的见解，以及传染性病毒颗粒丧失感染力的速度。我们建议如何在未来的模型中使用这些结果来描述具有缺陷干扰颗粒的合并感染，这些颗粒是一种新兴的替代治疗方法。
