Computational Models Growth & Development

The Role of Adaptive Strategies in Plant Modelling

How can plant modelers introduce a genetic dimension into physiological models at different organizational scales in a meaningful way?

The answer doesn’t need to be complicated, according to a new review article by François Tardieu, (INRAE, Montpellier), Graeme Hammer (University of Queensland) and coauthors published in in silico Plants.

According to the review, it is not feasible to build mechanistic models of action of every gene on traits in different environmental conditions, together with their integrative effect on yield. This ‘bottom-up’ approach, combining physiological mechanisms, leads to a near-infinite number of combinations and to an unmanageable number of parameters. For example, protein abundance, enzyme activity, and transcript abundance all respond to temperatures differently. Yet, plants have coordinated growth and development of different organs, suggesting a commonality of integrated temperature responses.

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Conversely, things get simpler at plant or canopy level. The authors write that the response curves of traits to environmental conditions are an example of a heritable parameter that is a result of natural selection rather than of a gene-driven coordination between temperature responses.

This provides the opportunity for simplification: because integrated adaptive traits are constrained into strategies by evolution, and are largely driven by feedback loops at high levels of integration, the use of simpler ‘meta-mechanisms’: robust and stable equations with heritable genotype-dependent parameters that can be used to predict the genetic variability of whole plant behavior.

Explains Tardieu, “these meta-mechanisms are unique and reproducible across a range of situations and their parameters have a heritability as high as those of equations describing detailed physiological mechanisms. For example, the heritability of the parameters of response curves are similar to those of maximal stomatal conductance in panels of maize hybrids.”

While detailed mechanistic models are preferred by upstream scientific communities, the use of meta-mechanisms place plant- and canopy- level models at the forefront due to their ability to robustly model the genetic variability of adaptation mechanisms to environmental cues.