Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
J Theor Biol. 2009 Nov 21;261(2):341-60. doi: 10.1016/j.jtbi.2009.07.038. Epub 2009 Aug 4.
We describe a mathematical model and Monte Carlo (MC) simulation of viral evolution during acute infection. We consider both synchronous and asynchronous processes of viral infection of new target cells. The model enables an assessment of the expected sequence diversity in new HIV-1 infections originating from a single transmitted viral strain, estimation of the most recent common ancestor (MRCA) of the transmitted viral lineage, and estimation of the time to coalesce back to the MRCA. We also calculate the probability of the MRCA being the transmitted virus or an evolved variant. Excluding insertions and deletions, we assume HIV-1 evolves by base substitution without selection pressure during the earliest phase of HIV-1 infection prior to the immune response. Unlike phylogenetic methods that follow a lineage backwards to coalescence, we compare the observed data to a model of the diversification of a viral population forward in time. To illustrate the application of these methods, we provide detailed comparisons of the model and simulations results to 306 envelope sequences obtained from eight newly infected subjects at a single time point. The data from 68 patients were in good agreement with model predictions, and hence compatible with a single-strain infection evolving under no selection pressure. The diversity of the samples from the other two patients was too great to be explained by the model, suggesting multiple HIV-1-strains were transmitted. The model can also be applied to longitudinal patient data to estimate within-host viral evolutionary parameters.
我们描述了一个病毒在急性感染期间进化的数学模型和蒙特卡罗(MC)模拟。我们同时考虑了新靶细胞病毒感染的同步和异步过程。该模型能够评估源自单一传播病毒株的新 HIV-1 感染中预期的序列多样性,估计传播病毒谱系的最近共同祖先(MRCA),并估计回合并到 MRCA 的时间。我们还计算了 MRCA 成为传播病毒或进化变体的概率。在排除插入和缺失的情况下,我们假设 HIV-1 在 HIV-1 感染的最早阶段(在免疫反应之前)通过碱基取代进化,没有选择压力。与遵循谱系回溯到合并的系统发育方法不同,我们将观察到的数据与病毒群体多样化的模型进行比较,该模型向前随时间进化。为了说明这些方法的应用,我们详细比较了模型和模拟结果与从八个新感染个体在单个时间点获得的 306 个包膜序列。来自 68 名患者的数据与模型预测非常吻合,因此与没有选择压力的单株感染进化兼容。另外两名患者样本的多样性太大,无法用模型解释,这表明有多种 HIV-1 株被传播。该模型还可以应用于纵向患者数据来估计宿主内病毒进化参数。