We develop a method for integrating time series expression profiles and factor-gene binding data to quantify dynamic aspects of gene regulation. We estimate latencies for transcription activation by explaining time correlations between gene expression profiles through available factorgene binding information. The resulting aligned expression profiles are subsequently clustered and again combined with binding information to determine groups or subgroups of co-regulated genes. The predictions derived from this approach are consistent with existing results (, ). Our analysis also provides several hypotheses not implicated in previous studies.