The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays

TL;DR

This paper develops probabilistic models for the statistical multiplicity of infection in virology assays, improving parameter inference accuracy across different concentration regimes and providing a web tool for practical application.

Contribution

It introduces new probabilistic models for SMOI in virus quantification and infectivity assays, enhancing existing methods and offering a web-based analysis tool.

Findings

Models improve parameter inference accuracy in simulations

Enhanced analysis across low and high viral concentrations

Web tool facilitates practical application of models

Abstract

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.