The method presented here for the determination of the infection parameters differs from previous mathematical modeling approaches to viral dynamics in that we have considered the explicit dependence of two experimental quantities on the parameters, rather than fitting a full dynamical model to the time-course of an experiment. There are a number of benefits to this approach. First, we have been careful to determine that the two experimental quantities under consideration, plaque velocity and viral titer growth rate, depend on only two unknown infection parameters, the infecting time, t inf , and the latent infection period, t L . This ensures that the problem of parameter extraction is theoretically solvable, which is often not the case when fitting a multi-parameter model to experimental data (see, e.g., [56] ). Second, the experimental quantities themselves are robust and easily measurable in repeated experiments. The viral plaque is formed by the progression of an infection wave across the monolayer of cells [42] whose constant velocity is determined by the infection kinetics averaged over many thousands of cells. Therefore the measured plaque velocity depends on the average interaction of virus and cell, and is insensitive to stochastic effects on a small scale. Similarly, the viral titer growth rate is due to the collective infection of thousands of cells and is independent of the details of initial infection (i.e., the precise value of the multiplicity of infection) or the total number of cells in the system. Other quantities of in vitro experiments, such as the time and value of the viral peak in a yield experiment, are much more sensitive to experimental details. Finally, the method we have applied here is robust to changes in the construction of the mathematical model itself. We have, for example, performed the same analysis of the plaque and yield assays using a stochastic model with more general assumptions about the cell transitions from latently infected to infectious and found nearly identical results (not shown here).