Multipartite viruses have two or more genome segments, and package different segments into different particle types. Although multipartition is thought to have a cost for virus transmission, its benefits are not clear. Recent experimental work has shown that the equilibrium frequency of viral genome segments, the setpoint genome formula (SGF), can be unbalanced and host-species dependent. These observations have reinvigorated the hypothesis that changes in genome-segment frequencies can lead to changes in virus-gene expression that might be adaptive. Here we explore this hypothesis by developing models of bipartite virus infection, leading to a threefold contribution. First, we show that the SGF depends on the cellular multiplicity of infection (MOI), when the requirements for infection clash with optimizing the SGF for virus-particle yield per cell. Second, we find that convergence on the SGF is very rapid, often occurring within a few cellular rounds of infection. Low and intermediate MOIs lead to faster convergence on the SGF. For low MOIs this effect occurs because of the requirements for infection, whereas for intermediate MOIs this effect is also due to the high levels of variation generated in the genome formula. Third, we explored the conditions under which a bipartite virus could outcompete a monopartite one. As the heterogeneity between environments and specificity of gene-expression requirements for each environment increased, the bipartite virus was more likely to outcompete the monopartite virus. Under some conditions changes in the genome formula helped to exclude the monopartite competitor, highlighting the versatility of the genome formula. Our results show the inextricable relationship between MOI and the SGF, and suggest that under some conditions the cost of multipartition can be outweighed by its benefits for the rapid tuning of viral gene expression.

This submission contains all the R code used for numerical predictions and simulations presented in the paper. Selected simulation results are also included, to allow the reader quick access to results without having to run some of these (computationally intensive) scripts.

The R code and simulation results are organized according to the figures presented in the data, to help easily find the relevant code.