Abstract
The cornerstone of today's plant virology consists of deciphering the molecular and mechanistic basis of host-pathogen interactions. Among these interactions, the onset of systemic infection is a fundamental variable in studying both within- and between-host infection dynamics, with implications in epidemiology. Here, we developed a mechanistic model using probabilistic and spatio-temporal concepts to explain dynamic signatures of virus systemic infection. The model dealt with the inherent characteristic of plant viruses to use two different and sequential stages for their within-host propagation: cell-to-cell movement from the initial infected cell and systemic spread by reaching the vascular system. We identified the speed of cell-to-cell movement and the number of primary infection foci in the inoculated leaf as the key factors governing this dynamic process. Our results allowed us to quantitatively understand the timing of the onset of systemic infection, describing this global process as a consequence of local spread of viral populations. Finally, we considered the significance of our predictions for the evolution of plant RNA viruses.