  Cellular populations are typically heterogenous collections of cells at
different points in their respective cell cycles, each with a cell cycle time
that varies from individual to individual. As a result, true single-cell
behavior, particularly that which is cell-cycle--dependent, is often obscured
in population-level (averaged) measurements. We have developed a simple
deconvolution method that can be used to remove the effects of asynchronous
variability from population-level time-series data. In this paper, we summarize
some recent progress in the development and application of our approach, and
provide technical updates that result in increased biological fidelity. We also
explore several preliminary validation results and discuss several ongoing
applications that highlight the method's usefulness for estimating parameters
in differential equation models of single-cell gene regulation.
