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 authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.

The study integrated genetic architecture derived from maize GWAS into phenotypic simulations of hybrid populations, using ≥200 top GWAS hits and adjusting marker effect sizes, which increased the correlation between simulated and empirical trait data across environments (r = 0.397–0.915). These informed simulations produced realistic trait distributions and genomic prediction results that closely matched empirical observations, demonstrating improved utility for digital breeding programs.