Tissue development and disease progression are multi-stage processes controlled by an evolving set of key regulatory factors, and identifying these factors necessitates a dynamic analysis spanning relevant time scales. Current omics approaches depend on incomplete biological databases to identify critical cellular processes. Herein, we present TRACER (TRanscriptional Activity CEll aRrays), which was employed to quantify the dynamic activity of numerous transcription factor (TFs) simultaneously in 3D and networks for TRACER (NTRACER), a computational algorithm that allows for cellular rewiring to establish dynamic regulatory networks based on activity of TF reporter constructs. We identified major hubs at various stages of culture associated with normal and abnormal tissue growth (i.e., ELK-1 and E2F1, respectively) and the mechanism of action for a targeted therapeutic, lapatinib, through GATA-1, which were confirmed in human ErbB2 positive breast cancer patients and human ErbB2 positive breast cancer cell lines that were either sensitive or resistant to lapatinib.