Stochastic, discrete modelling of in vitro and in vivo virus infections

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Context: While experimentation alone drives advances in virology, it has its limitations. Even within the relatively well-controlled environment of an in vitro cell culture, a virus infection is a complex, dynamical system. Disrupting a single mechanism experimentally can trigger several others, making it hard to isolate its function based on observations alone. There is also often an urgency to virology (need for antivirals, vaccines), making solution-driven research a priority over, and at the expense of, building an understanding of basic cell-virus interactions. Our research in the field of virophysics aims to fill these gaps through developing quantitative descriptions (i.e. mathematical and/or computer models, hereafter MCMs) of the key mechanisms at play during a virus infection. Our work is physics in its description and approach (and should be evaluated as such), but the systems it describes are virological in nature. Approach: Our MCMs represent the number of cells and virus particles (virions) as continuous variables, e.g. one virion infects 0.2 cells, and describe the deterministic, mean-field (MF) behaviour of what is in reality a discrete, stochastic (DS) system: one virion infects one cell (or not) 20% (or 80%) of the time. The proposed work builds on our well-established and validated MF-MCM frameworks with the overall goal to improve experimental design and result interpretation through rational, MCM-aided approaches. Objectives: To develop and apply a DS version of our MF-MCMs to identify and correct failures of the MF assumption in everyday virology to: evaluate and improve experiments meant to count infectious virions; resolve how the number of infectious virions in a sample relates to the number of infections it can cause; determine how virion infection failures and partial, abortive infections impact virus yield in co-infected cells; and explore how stochastic events over the course of a respiratory virus infection, such as coughing, alter its severity and pattern of spread within the human respiratory tract. More generally, through these objectives we aim to improve interpretation, statistical analysis, and visualization of experimental data in virology. Impact: This work could alter how MCMs use and interpret infectious virus concentrations measured experimentally over the time course of a virus infection. The ability to distinguish between a virus strain with a high rate of virion production and a low rate of cell infection per virion, and the opposite, can tell us about: what common replication process(es) might link all viruses that share one strategy over the other; or the replication bottleneck that should be targeted by antivirals. It could fundamentally alter how we view, quantify, and compare virus strain fitness or the efficacy and mode of action of antiviral interventions.