A systems biology approach to analyse leaf carbohydrate metabolism in Arabidopsis thaliana

Plant metabolic pathways are highly complex, comprising various branch points and crosslinks, and thus kinetic modelling turns up as an adequate tool to investigate regulatory principles. Recently, we presented a kinetic modelling approach to investigate core reactions of primary carbohydrate metabolism in photosynthetically active leaves of the model plant Arabidopsis thaliana [1] with an emphasis on the physiological role of vacuolar invertase, an enzyme that is involved in degradation of sucrose (Scr). This model was developed in an iterative process of modelling and validation. A final parameter set was identified allowing for simulation of the main carbohydrate fluxes and interpretation of the system behaviour over diurnal cycles. We found that Scr degradation by vacuolar invertase and re-synthesis involving phosphorylation of hexoses (Hex) allows the cell to balance deflections of metabolic homeostasis during light-dark cycles.In this study, we investigate the structural and stability properties of a model derived from the Scr cycling part of the metabolic pathway described in [1]. Based on the existing model structure, model parameters were repeatedly adjusted in an automated process applying a parameter identification algorithm to match the measured and simulated data. A method for statistical evaluation of the parameters and simulation results is introduced, which allows for the estimation of parameter variability. Statistical evaluation demonstrates that the same nominal concentration courses are predicted for different identification runs, while small variability in fluxes and larger variability in parameters can be observed. Further, the parameter identification results were analysed applying a principal component analysis (PCA). This leads to a more extensive investigation with respect to the extension and alignment of the parameter values in the parameter space. In addition, this allows for conclusions concerning the identifiability of the parameters a