Every node now represents a metabolite and every line describes a metabolite interconversion, i.e., enzymatic reaction. Based on this graphical representation, the mathematical model is then derived by translating nodes and lines in metabolite concentrations and enzymatic rate laws, for example the Michaelis-Menten equation. These rate equations are characterized by kinetic learn more parameters like enzymatic substrate affinity, Inhibitors,research,lifescience,medical i.e., the Michaelis-Menten constant KM, and the maximum enzyme activity vmax. This

process is crucial for the successful modeling approach as all further steps of mathematical analysis rely on these assumptions: if the interaction between two network components is described by equations or parameters which do not agree with Inhibitors,research,lifescience,medical confirmed experimental results, validation of simulation results by experimental data is not reliable anymore and the model becomes unfeasible. Although a vast number of metabolic interactions have intensively been characterized and many underlying laws of interaction are well known, like for example the Michaelis-Menten Inhibitors,research,lifescience,medical kinetics, deriving the most realistic model structure of a metabolic network becomes difficult

when assumptions about simplification have to be made. This is frequently the case for kinetic models, based on systems of ODEs, which are intended to provide an insight into the dynamics of metabolism. These dynamics are predominantly nonlinear and Inhibitors,research,lifescience,medical model systems are often characterized by a high-dimensional parameter space. Kinetic parameters, characterizing substrate affinity (KM) or inhibition (Ki),

are often not directly accessible to experimental measurements. In addition to the everlasting question how results of in vitro measurements differ from in vivo data, experimental conditions, like the temperature or pH, significantly Inhibitors,research,lifescience,medical constrain their validity. Hence, besides the determination of a model structure, the process of mathematical identification of unknown kinetic parameters represents another crucial step in building a realistic ODE-based model to simulate dynamics of plant metabolism. To reduce the complexity of and also the number of unknown kinetic parameters, individual enzymatic steps might be summarized in blocks of interconversions directly linking the metabolite concentrations that have been quantified. These blocks of interconversion are confined by the rate-limiting steps, i.e., the enzymatic reaction representing a regulatory bottleneck for the synthesis/degradation of a metabolite. Measurement on the kinetic parameters of the corresponding enzymes then allows for the estimation of the kinetic characteristics of this metabolic pathway. This approach was recently applied to the analysis of diurnal dynamics of the central carbohydrate metabolism in leaves of Arabidopsis thaliana [35].