Biophysical modelling as a tool for advancing plant science and engineering.

Area of Science:

• Plant Biology and Systems Biology
• Computational Biology and Mathematical Modeling

Background:

• Plant growth, development, and physiology involve intricate molecular, cellular, tissue, and organism-level processes.
• Experimental studies have elucidated key mechanisms in hormone signaling, gene regulation, metabolism, and environmental responses.
• Integrating diverse experimental data into a unified framework for understanding plant form and function remains a significant challenge.

Purpose of the Study:

• To review mathematical and computational modeling approaches for understanding plant biology.
• To highlight how modeling can address the challenge of integrating multi-scale experimental data.
• To propose combined modeling strategies for biotechnological applications in plant science.

Main Methods:

• Discussion of various modeling approaches: reaction-kinetic and Boolean networks (molecular/genetic regulation), mechanical and geometry-based models (tissue growth/morphogenesis), metabolic and constraint-based models (resource allocation), and hydraulic/electrophysiological models (transport processes).
• Emphasis on the integration of different modeling strategies.

Main Results:

• Mathematical and computational modeling offer powerful tools for simulating and predicting plant processes.
• Modeling provides strategic guidance for experimental research, identifying missing information.
• Combining diverse modeling approaches can lead to predictive tools for plant biotechnology.

Conclusions:

• Integrated modeling frameworks are essential for a mechanistic understanding of plant form and function.
• Modeling facilitates the development of predictive tools for enhancing plant stress tolerance, nutrient use efficiency, regenerative tissue engineering, and biomass productivity.
• This review advocates for a multi-model approach to advance plant science and biotechnology.