Creating a virtual leaf

Abstract

When microscopy meets modelling the exciting concept of a “virtual leaf” is born. The goal of a “virtual leaf” is to capture complex physiology in a virtual environment, resulting in the capacity to run experiments computationally. One example of a “virtual leaf” application is capturing three-dimensional (3D) anatomy from volume microscopy data and estimating where water evaporates in the leaf and the proportions of apoplastic, symplastic and gas phase water transport. The same 3D anatomy could then be used to improve established 3D reaction-diffusion models, providing a better understanding of the transport of CO2 across the stomata, through the airspace and across the mesophyll cell wall. This viewpoint discusses recent progress that has been made in transitioning from a bulk leaf approach to a 3D understanding of leaf physiology, in particular the movement of CO2 and H2O within the leaf.

The long and tortuous path towards improving photosynthesis by engineering elevated mesophyll conductance

“…However, despite being able to experimentally dissect gIAS from gcell, the underlying biological traits that determine these conductances remain uncertain. Conceptually, gIAS is more straightforward, as it is likely determined by the porosity, tortuosity and lateral path length of the IAS within leaves, which can all be measured using X-ray micro-CT scanning and other emerging techniques for 3D anatomical imaging (Earles et al, 2018;Harwood, 2023;Harwood et al, 2020;Lundgren et al, 2019;Théroux-Rancourt et al, 2017, 2021. In contrast, gcell will be determined by the conductance associated with CO2 transfer through the cell wall (gcw), plasma membrane (gpm), cytoplasm (gcyt) and chloroplast envelope (gchl) (Fig.…”

“…These data were then used to model a 'virtual leaf' which was scrutinised with a sensitivity analysis, to predict how changing a variety of anatomical traits would impact assimilation rates. Going forward, construction of these models in a greater range of crop species, with increasing levels of anatomical and biochemical detail, should be prioritised, to complement research estimating gm (Harwood, 2023). It is worth noting that whilst reactiondiffusion models can be integrated with the 3D arrangement of leaves, these models are only able to simulate leaf physiology, and therefore should not replace traditional data-driven estimations of gm.…”