School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA.
Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada.
Viruses. 2019 Apr 27;11(5):396. doi: 10.3390/v11050396.
Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen replication and immune system processes) and among-host dynamics (i.e., transmission). Within-host models, however, are not often fit to experimental data, which can serve as a robust method of hypothesis testing and hypothesis generation. In this study, we use mechanistic models and empirical, time-series data of viral titer to better understand the replication of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral replication and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: The viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, which can be validated in future studies.
机制模型对于理解宿主内动态（即病原体复制和免疫系统过程）和宿主间动态（即传播）至关重要。然而，宿主内模型通常与实验数据不匹配，而实验数据可以作为假设检验和假设生成的有力方法。在这项研究中，我们使用机制模型和病毒滴度的经验时间序列数据，以更好地理解蛙类宿主内蛙病毒的复制以及限制病毒复制的免疫动态。具体来说，我们将一套潜在的模型拟合到我们的数据中，每个模型都代表了病毒复制和免疫防御之间相互作用的假设。通过正式的模型比较，我们找到了一个简洁的模型，该模型能够捕捉到我们时间序列数据的关键特征：病毒滴度随时间上升和下降，可能是由于免疫系统的反应，而初始病毒剂量会影响病毒滴度峰值和峰值出现的时间。重要的是，我们的模型做出了一些预测，包括长期存在的病毒感染，这可以在未来的研究中得到验证。
