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| Biologists and Computer Scientists Identify Temporal Logic of Regulatory Genes Affecting Nitrogen Use Efficiency in Plants |
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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. |
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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.
Read more in the New York University news release. |
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