One way to improve crop yields is to improve photosynthesis, but how can scientists do this? Alexandrina Stirbet and colleagues review mathematical modelling as a tool for studying photosynthesis, as part of a special issue on Functional-Structural Plant Modelling in Annals of Botany.
“Oxygenic photosynthesis is a very important process, not only because it is the source of our food, fibre and many useful substances, but also because almost all life on the Earth depends on it, either directly or indirectly,” write Stirbet and colleagues. “Plants, algae and cyanobacteria are oxygenic photosynthetizers that use light energy to generate organic molecules [e.g. glucose (C6H12O6), sugars, starch] from carbon dioxide (CO2) and water (H2O), and release molecular oxygen (O2) into the atmosphere.”
The authors follow the photosynthetic process, from light providing the energy to strip hydrogen from water to add to carbon dioxide, to the production of metabolites. In the review they give examples of how modelling has helped provide information on how plants adapt to changing environments.
“From the examples discussed in this review, it is evident that correctly simplified but complete dynamic models of photosynthesis are well suited to obtaining information about how the photosynthetic organisms cope with variable environmental conditions,” the authors write. “Indeed, modelling is a very efficient method to identify important morphological and physiological parameters of a biological system and to find their optimal values. In addition, by using a larger variety of experimental data to verify such models, the simulations can lead to much more meaningful information about the organizational principles of the photosynthetic apparatus, which can also reveal original ways and means to improve the photosynthetic efficiency of plant crops…, besides being of theoretical interest. Moreover, multi-scale plant models (also known as plant system models), which quantitatively integrate physical, biochemical and physiological processes at different organizational levels (e.g. molecular, cell, organ, plant, population, or ecosystem), are able to predict physiological and growth properties of plants beyond photosynthetic metabolism, and they represent the future challenge in plant modelling.”