Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Area of Science:

• Plant biology
• Computational modeling
• Systems biology

Background:

• Computational models reveal gaps in understanding plant biological systems.
• Current single-scale models cannot capture emergent properties due to a lack of cross-scale interaction analysis.
• Optimizing cellular and organ-level plant architecture can increase productivity.

Purpose of the Study:

• To advocate for the integration of mathematical models across biological scales for accurate plant response prediction.
• To highlight the importance of computationally mimicking genome-to-phenome information flow for crop improvement.
• To address the challenge of connecting models across biological, temporal, and computational scales.

Main Methods:

• Perspective article outlining the necessity of integrated multi-scale modeling.
• Discussion of challenges in connecting models across scales and interpreting outputs.
• Highlighting the efforts of the international Crops in silico consortium.

Main Results:

• Single-scale models are insufficient for predicting plant responses in untested environments.
• Integrated multi-scale models are necessary to capture emergent properties and cross-scale interactions.
• Connecting models across scales is crucial for discovering new experimental strategies.

Conclusions:

• Integrating mathematical models across biological scales is vital for predicting plant behavior and enhancing crop development.
• Computational approaches are key learning tools that guide experimental design and measurements.
• The Crops in silico consortium is working to overcome challenges in multi-scale plant modeling.