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 applied a progressive, sublethal drought treatment to Arabidopsis thaliana, collecting time‑resolved phenotypic and transcriptomic data. Machine‑learning analysis revealed distinct drought stages driven by multiple overlapping transcriptional programs that intersect with plant aging, and identified high‑explanatory‑power transcripts as biomarkers rather than causal agents.