Rapid reprogramming of transcriptional networks enables cells to dynamically adapt to a changing environment. However, most gene regulatory networks (GRNs) are captured and modeled as a static web of interactions. To fill this gap, Gloria and colleagues’ studies exploit TIME – the relatively unexplored dimension of GRNs – to uncover the temporal transcriptional logic that underlies dynamic nitrogen (N) signalling in plants. Because causality moves forward in time, fine-scale time-series data of genome-wide N-responses enables us to infer and validate GRN models that are able to predict TF -> target relationships in out-of-sample data – the ultimate goal of systems biology. More broadly, the time-based approaches we develop and deploy can be applied to uncover the temporal transcriptional logic for any signalling response system in biology, agriculture or medicine.