e., the result of model

simulation [26]. Additionally, in many cases mechanisms of allosteric regulation are known but quantitative experiments on parameters like substrate affinity (KM) or inhibitory constants (Ki) are lacking. This enforces the application of parameter estimation to calculate parameter values which are either completely unknown or can be estimated within numerical bounds Inhibitors,research,lifescience,medical based on published data on a different condition or organism. Indeed, such assumptions cause uncertainties, which have to be discussed carefully when interpreting the model output. However, although there might be several uncertainties with respect to regulatory instances involved in every single reaction of metabolism, numerous studies have proven kinetic modeling to be a promising approach to comprehensively analyze complex processes in plant biology. An overview of applications is given by Schallau and Junker [27] exemplarily Inhibitors,research,lifescience,medical comprising the process of photosynthesis [28], leaf carbon metabolism [29], sucrose metabolism Inhibitors,research,lifescience,medical in sugar cane (Saccharum officinarum) [30] or the aspartic acid-derived amino

acid pathway in Arabidopsis thaliana [31]. In contrast to kinetic modeling, the approach of structural modeling is based on the idea of Selleck GF109203X constructing models without kinetic information. This modeling approach refers only to the stoichiometry of the reactions within the system which is summarized Inhibitors,research,lifescience,medical in the stoichiometric matrix N. Considering a metabolic reaction network, each column of N represents a reaction while

rows represent metabolites. Inhibitors,research,lifescience,medical Hence, the elements of N describe the stoichiometric coefficients of metabolites in the reactions. Positive entries indicate that the metabolite is produced by the reaction, while negative values indicate consumption. Entries of zero indicate that the metabolite is not involved in this reaction. The definition of a vector v containing the rates of metabolite interconversions allows for the description of the steady state of the metabolic reaction network by a set of differential equations: (1) where M represents a matrix Electron transport chain containing metabolite concentrations and t is time. Solutions of this equation can be calculated applying linear algebraic rules. The advantage of this approach becomes obvious when considering the large number of reactions in a metabolic system, which can be predicted from an annotated genome sequence. The workflow from metabolic reconstruction to modeling of a metabolic network based on an annotated genome sequence was previously described in detail [32].