The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study isolated the Plant Cysteine Oxidase/Ethylene Response Factor VII oxygen‑sensing circuit from Arabidopsis thaliana and reconstituted it in Saccharomyces cerevisiae, using a reporter to compare hypoxia‑induced transcriptional dynamics in yeast and plants. Both systems showed rapid ERFVII stabilization, but plants exhibited a larger response, which could be enhanced in yeast by adding a hypoxia‑inducible feedback loop. Computational modeling identified promoter competition and hypoxia‑inducible PCOs as key determinants of early hypoxia responses.