Determining how cells regulate their transcriptional response to extracellular signals is key to the understanding of complex eukaryotic systems. This study was initiated with the goals of furthering the study of mammalian transcriptional regulation and analyzing the relative benefits of related computational methodologies. One dataset available for such an analysis involved gene expression profiling of the early growth factor response to platelet derived growth factor (PDGF)in a human glioblastoma cell line; this study differentiated genes whose expression was regulated by signaling through the phosphoinositide-3-kinase (PI3K) versus the extracellular-signal regulated kinase (ERK) pathways. We have compared the inferred transcription factors from this previous study with additional predictions of regulatory transcription factors using two alternative promoter sequence analysis techniques. This comparative analysis, in which the algorithms predict overlapping,although not identical, sets of factors, argues for meticulous benchmarking of promoter sequence analysis methods to determine the positive and negative attributes that contribute to their varying results. Finally, we inferred transcriptional regulatory networks deriving from various signaling pathways using the CARRIE program suite. These networks not only included previously described transcriptional features of the response to growth signals, but also predicted new regulatory features for the propagation and modulation of the growth signal.