mathematical-modeling
Mathematical modeling uses abstract mathematical frameworks to represent and simulate complex biological systems and processes. In plant science, these models help researchers quantify and predict phenomena such as growth dynamics, nutrient uptake, photosynthesis, and responses to environmental stress that would be difficult or impossible to study through experiments alone. By translating biological mechanisms into equations, mathematical models allow scientists to test hypotheses, optimize agricultural practices, and gain mechanistic insights into how plants function and adapt.
open_in_new WikipediaPubMed · 2026-01-01
This review surveys how computer models—from simple equations to machine learning—are being used to predict how plants respond to drought, heat, and salt stress. By synthesizing these tools' strengths and gaps, the authors chart a path toward faster development of stress-resilient crops.
At least four distinct classes of models—empirical, statistical, mechanistic, and process-based—plus emerging machine learning methods are currently applied to plant stress biology, each capturing different scales from single cells to whole crops.
A critical unresolved challenge is modeling combined stresses (e.g., simultaneous drought and heat), which interact in non-linear ways that single-stress models consistently fail to represent.
Integrating omics data (genomics, proteomics, metabolomics) with physiological models is identified as the most promising frontier for improving predictive accuracy under variable real-world conditions.