1 Introduction Principled methods for estimating unobserved time-points,clustering, and aligning microarray gene expression timeseries are needed to make such data useful for detailed anal-ysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational biologists. For example, Dynamic Bayesian Networks may be usedto build models and try to understand how genetic responses unfold. However, such modeling frameworks need a suf-ficient quantity of data in the appropriate format. Current gene expression time-series data often do not meet these re-quirements, since they may be missing data points, sampled non-uniformly, and measure biological processes that exhibittemporal variation.