感染的统计多重性对病毒定量和免疫活性测定的影响
The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays
摘要：
在病毒学中采用许多生物测定来量化感兴趣的参数。两类这样的测定，病毒定量测定（VQA）和感染性测定（IA），旨在分别估计溶液中存在的病毒数量和病毒株成功感染宿主细胞的能力。VQA在极稀浓度下操作，结果可能会受到病毒-细胞相互作用的随机变异性的影响。在另一个极端，在IA中使用高病毒颗粒浓度，导致大量病毒感染每个细胞，足以使总转录活性发生可测量的变化。此外，宿主细胞可以在任何浓度方案下被多种颗粒感染，导致感染的统计学多重性（SMOI），并在测定信号和参数估计值中产生潜在的显著变异性。我们开发了在低和高病毒颗粒浓度限值下的SMOI概率模型，并将其应用于空斑（VQA）、终点稀释（VQA）和荧光素酶报告基因（IA）检测。一个基于网络的工具，实现我们的模型和分析也开发和介绍。我们测试我们提出的新方法推断实验参数的数据，使用数值模拟和现有的程序在所有限制的改进。
英文摘要：
Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQA) and infectivity assays (IA), aim to estimate the number of viruses present in a solution, and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection (SMOI) and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for SMOI at low and high viral particle concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits.
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