Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Chair of Bioprocess Engineering, Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Magdeburg, Germany.
PLoS Comput Biol. 2019 Feb 19;15(2):e1006819. doi: 10.1371/journal.pcbi.1006819. eCollection 2019 Feb.
Influenza A viruses (IAV) are commonly used to infect animal cell cultures for research purposes and vaccine production. Their replication is influenced strongly by the multiplicity of infection (MOI), which ranges over several orders of magnitude depending on the respective application. So far, mathematical models of IAV replication have paid little attention to the impact of the MOI on infection dynamics and virus yields. To address this issue, we extended an existing model of IAV replication in adherent MDCK cells with kinetics that explicitly consider the time point of cell infection. This modification does not only enable the fitting of high MOI measurements, but also the successful prediction of viral release dynamics of low MOI experiments using the same set of parameters. Furthermore, this model allows the investigation of defective interfering particle (DIP) propagation in different MOI regimes. The key difference between high and low MOI conditions is the percentage of infectious virions among the total virus particle release. Simulation studies show that DIP interference at a high MOI is determined exclusively by the DIP content of the seed virus while, in low MOI conditions, it is predominantly controlled by the de novo generation of DIPs. Overall, the extended model provides an ideal framework for the prediction and optimization of cell culture-derived IAV manufacturing and the production of DIPs for therapeutic use.
甲型流感病毒（IAV）常用于感染动物细胞培养物进行研究和疫苗生产。它们的复制受感染复数（MOI）的强烈影响，MOI 范围根据各自的应用而异，跨越几个数量级。到目前为止，IAV 复制的数学模型很少关注 MOI 对感染动力学和病毒产量的影响。为了解决这个问题，我们用明确考虑细胞感染时间点的动力学扩展了一个现有的贴壁 MDCK 细胞中 IAV 复制的模型。这种修改不仅能够拟合高 MOI 测量值，而且还能够使用相同的参数集成功预测低 MOI 实验的病毒释放动力学。此外，该模型还可以研究不同 MOI 下缺陷干扰颗粒（DIP）的传播。高 MOI 和低 MOI 条件之间的关键区别在于总病毒粒子释放中传染性病毒粒子的百分比。模拟研究表明，高 MOI 下的 DIP 干扰仅由种子病毒中的 DIP 含量决定，而在低 MOI 条件下，DIP 干扰主要由 DIP 的从头产生控制。总体而言，扩展模型为预测和优化细胞培养衍生的 IAV 制造以及用于治疗用途的 DIP 生产提供了理想的框架。
