The plaque growth experiment yields a single experimental measure for each strain: the plaque velocity. The multiple-cycle viral yield assay provides two quantities: the viral titer growth rate, and the viral titer decay rate. It is the goal of this paper to associate these broad experimental measures to the values of fundamental infection parameters, specific to each strain, which quantitatively characterize replicative efficiency. To this aim, we have constructed mathematical models which allow us to simulate each in vitro assay in a computer experiment. The basic mathematical model used here is similar to other within-host models of viral infection [31] [32] [33] [34] [35] [36] . A cell can be in one of four states -target (uninfected), latently infected (infected but not yet releasing virus), infectious (releasing virus) and dead (no longer releasing virus) -and its passage through these states ( Figure 4 ) is determined by five infection kinetics parameters. Target cells interacting with virus become latently infected at a constant infection rate per virus, b. The average time a cell remains latently infected is called the latent infection period, t L , and the average time a cell releases virus is called the infectious lifespan, t I . Virus is produced by infectious cells at a constant viral production rate, p, and this free virus loses infectivity exponentially at a constant rate of viral infectivity loss, c (as is observed in experiments [35] ). In applying this model, it is assumed that the growth of a particular influenza virus strain in a particular cell line is determined by a single, unique set of values for these five parameters. Thus, although the mathematical structure of the models used for each experimental assay is different (see Materials and Methods for a detailed description of each), the parameter values for a particular virus strain are assumed to be constant from assay to assay. With only three experimentally measured quantities, it would be impossible to uniquely identify all five parameters for a particular virus strain. Fortunately, it is possible to reduce the number of parameters considered and obtain unique identification of a few key parameter values.