Further kinetic characterization of the non-allosteric phosphofructokinase from Escherichia coli K-12

KINETIC MODEL OF PHOSPHOFRUCTOKINASE-1 FROMESCHERICHIA COLI

This paper presents a kinetic model of phosphofructokinase-1 from Escherichia coli. A complete catalytic cycle has been reconstructed based on available information on the oligomeric structure of the enzyme and kinetic mechanism of its monomer. Applying the generalization of the Monod–Wyman–Changeux approach proposed by Popova and Sel'kov35–37to the reconstructed catalytic cycle rate equation has been derived. Dependence of the reaction rate on pH , magnesium, and effectors has been taken into account. Kinetic parameters have been estimated via fitting the rate equation against experimentally measured dependencies of initial rate on substrates, products, effectors, and pH available from the literature. The model of phosphofructokinase-1 predicts (1) cooperativity of binding both fructose-6-phosphate and ATPMg2-, (2) significant inhibition of the enzyme resulting from an increase in total concentration of ATP under the condition of fixed concentration of Mg2+ions, and (3) dual effect of ADP consisting of allosteric activation and product inhibition of the enzyme. Moreover, the model developed can be used in the kinetic modeling of biochemical pathways containing phosphofructokinase-1.

Translating biochemical network models between different kinetic formats

Mechanistic biochemical network models describe the dynamics of intracellular metabolite pools in terms of substance concentrations, stoichiometry and reaction kinetics. Data from stimulus response experiments are currently the most informative source for in-vivo parameter estimation in such models. However, only a part of the parameters of classical enzyme kinetic models can usually be estimated from typical stimulus response data. For this reason, several alternative kinetic formats using different "languages" (e.g. linear, power laws, linlog, generic and convenience) have been proposed to reduce the model complexity. The present contribution takes a rigorous "multi-lingual" approach to data evaluation by translating biochemical network models from one kinetic format into another. For this purpose, a new high-performance algorithm has been developed and tested. Starting with a given model, it replaces as many kinetic terms as possible by alternative expressions while still reproducing the experimental data. Application of the algorithm to a published model for Escherichia coli's sugar metabolism demonstrates the power of the new method. It is shown that model translation is a powerful tool to investigate the information content of stimulus response data and the predictive power of models. Moreover, the local and global approximation capabilities of the models are elucidated and some pitfalls of traditional single model approaches to data evaluation are revealed.