Iron plays a crucial role in plant processes including photosynthesis and nitrogen assimilation. Iron deficiency leads to leaf yellowing, stunted growth and drastic yield losses. This problem underpins the urgency to develop cultivars that can be more efficient in iron uptake, thus increasing plant nutritional value.
A recent article published by in silico Plants uses dynamic modeling techniques to obtain a deeper understanding of the complex biological processes involved in iron deficiency response in plants.
“The ability to precisely modulate a plant’s response to iron deficiency at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues,” says co-author Dr. Terri Long, Associate Professor of Molecular Plant Biology at North Caroline State University
This article presents an approach for the systematic integration of biological datasets and provides a mathematical model that describes and predicts changes in gene expression in response to iron deficiency. According to co-author Dr. Cranos Williams, Associate Professor of Electrical and Computer engineering at North Caroline State University, “The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behavior of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors, and predict the effect of double regulator mutants.”
The model is a starting point in exploring transcriptome dynamics associated with nutritional stress and has the potential to replace in-planta experiments with in-silico simulations in an effort to engineer a desired phenotype