A research team of biologists and computer scientists has adopted a
time-based machine-learning approach to deduce the temporal logic of
nitrogen signaling in plants from genome-wide expression data. The research
is centered on gene regulatory networks (GRNs) that identify which
transcription factors serve to regulate genes needed to respond to nitrogen,
a nutrient vital to plant development and human nutrition.

The research used time, which is the fourth and largely unexplored dimension
of GRNs, to better explain the transcription factors (TFs) relevant to
genetic responses to nitrogen. Understanding how transcription factors
function at different points in time allows scientists to target the early
responders and to make predictions on the temporal operation of the entire
gene regulatory network.

The time-based GRN provides regulatory knowledge to inform testable
hypotheses on how 155 transcription factors exert regulatory control of
nitrogen response and its effect on core plant life processes, including
circadian rhythm, photosynthesis, and RNA metabolism, among other phenomena
affecting plant growth, development, and yield.

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