Professor Andrew Fleming
University of Sheffield
A 3D model of photosynthesis to inform on rice leaf performance.
A 3-D Model of Photosynthesis to Inform Breeding for Improved Rice Performance in a Changing Climate.
The aim of this project is to generate the first 3D model of photosynthesis for a rice leaf and to use this model to identify aspects of leaf cellular architecture which can be selected or manipulated to improve photosynthesis. In particular, we will use the model to predict appropriate rice leaf architectures for future climate change scenarios in which global CO2 level is expected to increase significantly, thus enabling breeders to select for appropriate genotypes to future-proof this vital crop.
Engineering The Rice Leaf for Improved Photosynthesis.
The aim of this project is to prolong the phase of rice leaf maturation, developing a leaf chassis more amenable to engineering improved photosynthesis. As a result of previous project we have developed a unique transcriptomic resource, coupled with a precise framework describing rice leaf development. In this project we will build on this foundation to identify lead transcription factors and cell cycle genes implicated in maintaining rice leaves in a juvenile phase of development. Can targetting juvenile regulators to later phases of development extend the maturation phase of rice leaves, thus enabling more fundamental tissue engineering?
Using metabolite imaging to test a 3D spatial model of photosynthesis.
We have developed a new model of photosynthesis in rice which incorporates the spatial distribution of metabolism. In this project we will develop a DESI-based method for the visualization of photosynthetic-related metabolites in rice leaves. These data will be used to parameterize the extant 3D spatial model of photosynthesis, leading to outputs of predicted photosynthetic performance under present and future levels of CO2. These model predictions will be compared with actual measures of performance (derived from A/Ci and A/Q curves via fluorescence-linked gas exchange) of plants under both ambient and elevated levels of CO2. The data will both inform and test the utility of a model to identify features of rice leaves that might be optimized for future leaf photosynthetic performance.