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Mathematical modeling of the central carbohydrate metabolism in Arabidopsis reveals a substantial regulatory influence of vacuolar invertase on whole plant carbon metabolism.

Plant Physiol.2010 May;153(1):260-72. Epub 2010 Mar 05
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摘要


A mathematical model representing metabolite interconversions in the central carbohydrate metabolism of Arabidopsis (Arabidopsis thaliana) was developed to simulate the diurnal dynamics of primary carbon metabolism in a photosynthetically active plant leaf. The model groups enzymatic steps of central carbohydrate metabolism into blocks of interconverting reactions that link easily measurable quantities like CO(2) exchange and quasi-steady-state levels of soluble sugars and starch. When metabolite levels that fluctuate over diurnal cycles are used as a basic condition for simulation, turnover rates for the interconverting reactions can be calculated that approximate measured metabolite dynamics and yield kinetic parameters of interconverting reactions. We used experimental data for Arabidopsis wild-type plants, accession Columbia, and a mutant defective in vacuolar invertase, AtbetaFruct4, as input data. Reducing invertase activity to mutant levels in the wild-type model led to a correct prediction of increased sucrose levels. However, additional changes were needed to correctly simulate levels of hexoses and sugar phosphates, indicating that invertase knockout causes subsequent changes in other enzymatic parameters. Reduction of invertase activity caused a decline in photosynthesis and export of reduced carbon to associated metabolic pathways and sink organs (e.g. roots), which is in agreement with the reported contribution of vacuolar invertase to sink strength. According to model parameters, there is a role for invertase in leaves, where futile cycling of sucrose appears to have a buffering effect on the pools of sucrose, hexoses, and sugar phosphates. Our data demonstrate that modeling complex metabolic pathways is a useful tool to study the significance of single enzyme activities in complex, nonintuitive networks.

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