The Role of Mathematical Modeling in Plant Abiotic Stress Biology: Current Trends and Future Prospects.
Bhavana S, Rao PR, Latha P, Bindu GSM, Kumar MV
Climate Adaptation
The wheat, corn, and vegetables feeding you are increasingly failing under record droughts and heat waves—better predictive models let scientists design tougher crop varieties years faster than traditional trial-and-error field breeding.
Plants deal with stress—too little water, too much heat, or salty soil—through incredibly complex chains of reactions that are hard to study in a lab alone. Scientists are now using computer models and AI to simulate these reactions and predict how plants will cope before they even step outside. This review maps out which modeling tools work best, where the gaps are, and how combining biology knowledge with big data could help us grow food in a changing climate.
Key Findings
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.
chevron_right Technical Summary
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.
Abstract Preview
Plant responses to abiotic stress are governed by complex interactions operating across physiological, biochemical, and molecular levels, which remain difficult to interpret using experimental appr...
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