Becuse it can be difficult to make measurements on single cells, populations of cells are often used to study dynamic processes such as cell division.  Although various methods can be used to synchronize cell populations in discrete cell cycle phases, upon release from the synchronization, the underlying population loses synchrony and becomes distributed across multiple cell phases.  Thus, quantitative measurements taken as the cell population traverses the cell cycle represent a convolution of a true value along with values associated with cells distributed in other cell cycle phases.  Using the budding yeast, S. cerevisiae as a model system, we present a novel and flexible mathematical model that describes the dynamics of population distributions resulting synchrony loss over time.  Using bud emergence as a measured parameter, the model captures multiple sources of synchrony loss, including effects related to cell reproduction.  We show that this model reliably fits data collected from populations synchronized by multiple techniques and can predict cell cycle distributions as measured by flow cytometric analysis of DNA content.  Additionally, we show that the model can be used to deconvolve quantitative data from time-course experiments.