We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.