The Role of Mathematical Modeling in Plant Abiotic Stress Biology: Current Trends and Future Prospects

Plant responses to abiotic stress are governed by complex interactions operating across physiological, biochemical, and molecular levels, which remain difficult to interpret using experimental approaches alone. Increasing frequency and intensity of stresses such as drought, salinity, and heat further highlight the need for integrative tools that can capture these dynamics and support predictive understanding. Mathematical modeling has therefore become an important approach for analyzing plant stress responses across multiple scales. This review examines the range of modeling frameworks applied in plant abiotic stress biology, including empirical, statistical, mechanistic, and process-based models, along with emerging machine learning approaches. Their capacity to represent plant responses from cellular processes to whole-plant and crop-level behavior is discussed, with particular attention to their application under single and combined stress conditions. Recent developments in integrating physiological knowledge with omics-driven data and data-driven modeling approaches are also considered. Current applications in crop improvement, stress assessment, and climate-resilient agricultural planning are evaluated, alongside key limitations such as parameter uncertainty, scale integration, and representation of interacting stresses. The review further identifies directions for developing more robust and biologically grounded modeling frameworks that can improve predictive capability under variable environmental conditions. By synthesizing existing approaches and highlighting current gaps, this article aims to support the advancement of modeling strategies in plant abiotic stress research.