We present a new approach to segmenting multiple time series by analyzing the dynamics of cluster rearrangement around putative segment boundaries. By directly minimizing information-theoretic measures of segmentation quality derived from Kullback-Leibler (KL) divergences, our formulation reveals clusters of genes along with a segmentation such that clusters show concerted behavior within segments but exhibit significant re- grouping across segmentation boundaries. This approach finds application in distilling large numbers of gene expression profiles into temporal relationships underlying biolog- ical processes. The results of the segmentation algorithm can be summarized as Gantt charts revealing temporal dependencies in the ordering of key biological processes. Ap- plications to the yeast metabolic cycle and the yeast cell cycle are described.