Determining the best sampling rates (which maximize information yield and minimize cost) for time-series high-throughput gene expression experiments is a challenging optimization problem. Although existing approaches provide insight into the design of optimal sampling rates, our ability to utilize existing differential gene expression data to discover optimal timepoints is compelling.