Syracuse

Metabolic modeling helps to better understand the behavior of complex biological systems by integrating knowledge from multiple levels of the system. It combines omics and physiological data to reconstruct metabolic networks and predict fluxes of matter and biomass production depending on the applied environmental conditions (e.g., nutrition). Metabolic modeling has already proven useful in the field of bioprocess engineering, where understanding metabolism has enabled bioengineers to optimize microbial growth and increase yields industrial products. Metabolic modeling was applied to plants for nearly a decade now and allowed to gain insight in some mechanisms such as the possible roles of photorespiration. However, very few models considered the multi-organ structure of the plant, which influence greatly its metabolism and its response to environmental (e.g. nutrition) or internal (e.g. mutations) perturbations. We developed a multi-organ genomescale metabolic model of a tomato plant (Solanum lycopersicum) at the vegetative growth stage. The model combines metabolic networks of leaf, stem and root. Exchanges are performed by xylem and phloem sap. The model was calibrated with experiments gathering physiological data (growth, transpiration) and metabolomics (biomass composition, xylem sap chemistry). The model allowed us to explore the metabolic flux distribution in the different organs and saps. It showed, for the first time, what might explain the organic composition of xylem sap and in particular the predominance of glutamine. The model also allowed to measure the cost of the stem in terms of carbon sink. Finally, the model was used to predict plant responses to different perturbations including the effects of nitrogen nutrition on growth, the impact of mutations or the presence of a plant pathogen. In each case, the predictions were consistent with experimental studies, showing that the model is accurate and can be a useful tool to decipher how internal or external perturbations impact plant metabolism.