A 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 work potentially offers new ways to monitor and enhance crop growth using less nitrogen fertilizer, which would benefit human nutrition and the environment.
A 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 work potentially offers new ways to monitor and enhance crop growth using less nitrogen fertilizer, which would benefit human nutrition and the environment.
The research, which appears in the journal Proceedings of the National Academy of Sciences (PNAS), centers on gene regulatory networks (GRNs) that identify which transcription factors serve to regulate genes needed to respond to nitrogen, which is a nutrient vital to plant development and human nutrition.
“By constructing these regulatory networks based on dynamic gene responses to nitrogen treatment, we can see, in time-lapse detail, the genetic process necessary for the intake of nitrogen and its conversion into amino acids used in the synthesis of all N-containing compounds including DNA, proteins, and chlorophyll,” explains Gloria Coruzzi, a professor in NYU’s Department of Biology and Center for Genomics and Systems Biology and the paper’s senior author. “Armed with these new insights, we can now look ahead for ways to bolster the efficiency of food production and enrich sustainable agriculture measures on lower nitrogen input, which would benefit the environment.”
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