Data-driven integrative modeling of photorespiratory metabolism

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Biological processes involved in photorespiratory and photosynthetic metabolisms operate concurrently and affect the interplay between carbon and nitrogen assimilation reflected in plant growth. Experimental evidence has indicated that photorespiratory metabolism has a wide-ranging influence not only on other principal metabolic pathways but also on a multitude of signaling cascades. Therefore, accurate quantitative models of photorespiration can provide the means for predicting and in silico probing of behavior on various levels of the plant system. The goal of this project is threefold: First, we plan to devise both structural and kinetic models of photorespiration which will be used to in silico investigate the effects of photorespiratory metabolism under different environmental conditions on genome-scale metabolic network of C3 plants. Second, the high-throughput transcriptomics and metabolomics data from experiments with wild type and mutant plants will be integrated with the models to characterize the cellular metabolic state, described by reaction fluxes and metabolite levels, and determine key pathway components. The integration of data and models will be performed by using constraint-based approaches for estimating fluxes and metabolite turnover. With the help of these approaches, we will consider photoresipratory metabolism in the context of tightly linked carbon and nitrogen metabolisms. Third, the kinetic and structural models will be employed to design and investigate in silico metabolic engineering strategy to improve the plant biomass production. The proposed framework will allow assessing the still controversial role of photorespiration in plant growth and yield by confronting the devised models with high-throughput data.