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An approach to multiscale modelling with graph grammars.

Yongzhi Ong, Katarína Streit, Michael Henke

Annals of Botany

|August 20, 2014

Summary

This study introduces a multiscale modeling approach for functional-structural plant models (FSPMs), demonstrating its viability and advantages over single-scale methods for simulating plant dynamics.

Area of Science:

Plant modeling
Computational biology
Ecological modeling

Background:

Functional-structural plant models (FSPMs) simulate plant processes across scales.
Current FSPMs lack clarity on the benefits of multiscale dynamics.
Other scientific fields show advantages of multiscale modeling.

Purpose of the Study:

Introduce a novel multiscale modeling approach for FSPMs.
Provide a conceptual framework for scale-to-scale interactions.
Enable alternative model development beyond single-scale correlations.

Main Methods:

Revisit a three-part graph data structure and grammar.
Develop a conceptual framework for multiscale modeling.
Implement methods in the XL programming language, supporting reverse information flow.

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Main Results:

Demonstrate multiscale modeling with three example FSPMs.
Illustrate probabilistic modeling for organ-level dynamics and crown growth.
Model juvenile beech stands under ozone exposure using multiscale topology and metabolic simulations.

Conclusions:

The graph data structure effectively supports multiscale data representation and grammar operations.
Multiscale modeling in FSPMs is a viable alternative to single-scale, correlation-based approaches.
Further research is motivated by illustrated advantages and disadvantages, focusing on sensitivity analysis and efficiency.