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Many biological assays are employed in virology to quantify parameters ofinterest. Two such classes of assays, virus quantification assays (VQA) andinfectivity assays (IA), aim to estimate the number of viruses present in asolution, and the ability of a viral strain to successfully infect a host cell,respectively. VQAs operate at extremely dilute concentrations and results canbe subject to stochastic variability in virus-cell interactions. At the otherextreme, high viral particle concentrations are used in IAs, resulting in largenumbers of viruses infecting each cell, enough for measurable change in totaltranscription activity. Furthermore, host cells can be infected at anyconcentration regime by multiple particles, resulting in a statisticalmultiplicity of infection (SMOI) and yielding potentially significantvariability in the assay signal and parameter estimates. We developprobabilistic models for SMOI at low and high viral particle concentrationlimits and apply them to the plaque (VQA), endpoint dilution (VQA), andluciferase reporter (IA) assays. A web-based tool implementing our models andanalysis is also developed and presented. We test our proposed new methods forinferring experimental parameters from data using numerical simulations andshow improvement on existing procedures in all limits.

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