Exploring the effect of biological delays in kinetic models of influenza within a host or cell culture.
Exploring the effect of biological delays in kinetic models of influenza within a host or cell culture.
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DOI：
10.1186/1471-2458-11-s1-s10
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发表时间：
2011-02-25
影响因子：
4.5
通讯作者：
Beauchemin CA
中科院分区：
文献类型：
--
作者：
Holder BP;Beauchemin CA
对于典型的体内流感感染，随时间推移的病毒滴度的特征在于1-2天的指数生长，随后是指数衰减。这种简单的动态可以通过广泛的数学模型再现，这使得模型选择和从实验数据中提取生物相关的感染参数变得困难。我们分析了文献中的体外实验数据，特别是单循环病毒产量实验数据，以缩小现实感染模型的范围。特别地，我们证明了对于细胞在给定感染状态下花费的时间使用正态或对数正态分布的可行性（例如，新感染的细胞在开始产生病毒之前处于潜伏状态所花费的时间），同时暴露了隐含地利用指数分布的常微分方程模型和具有固定长度延迟的延迟微分方程模型的缺点。通过拟合已发表的来自人类志愿者挑战实验的病毒滴度数据，我们表明替代模型可以导致关键感染参数的不同估计。
For a typical influenza infection in vivo, viral titers over time are characterized by 1–2 days of exponential growth followed by an exponential decay. This simple dynamic can be reproduced by a broad range of mathematical models which makes model selection and the extraction of biologically-relevant infection parameters from experimental data difficult. We analyze in vitro experimental data from the literature, specifically that of single-cycle viral yield experiments, to narrow the range of realistic models of infection. In particular, we demonstrate the viability of using a normal or lognormal distribution for the time a cell spends in a given infection state (e.g., the time spent by a newly infected cell in the latent state before it begins to produce virus), while exposing the shortcomings of ordinary differential equation models which implicitly utilize exponential distributions and delay-differential equation models with fixed-length delays. By fitting published viral titer data from challenge experiments in human volunteers, we show that alternative models can lead to different estimates of the key infection parameters.