Uncovering modeling features of viral replication dynamics from high-throughput single-cell virology experiments

Abstract

AbstractViruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to produce offspring. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to growth data from high-throughput single-cell poliovirus infection experiments. The model’s mechanistic parameters provide estimates of several aspects associated with the virus’s intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.Author SummaryUnderstanding the intercellular processes associated with virus replication is essential for controlling viral diseases. Single-cell infection experiments with poliovirus have shown that viral growth differs among individual cells in terms of both the total amount of virus produced and the rate of viral production. To better understand the source of this variation we here develop a modeling protocol that simulates viral growth and we then fit our model to data from high-throughput single-cell experiments. Our modeling approach is based on generating time course simulations of populations of single-cell infections. We aggregate metrics from our simulated populations such that we can describe our simulated viral growth in terms of several distributions. Using the corresponding distributions calculated from experimental data we minimize the difference between our simulated and experimental data to estimate parameters of our simulation model. Each parameter corresponds to a specific aspect of the intercellular viral replication process. By examining our estimates of these parameters, we find that steps that occur early in the viral replication process are essential for our model to capture the variability observed in experimental data.