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Robustness in Escherichia coli glutamate and glutamine synthesis studied by a kinetic model.

ABSTRACT: Metabolic control of glutamine and glutamate synthesis from ammonia and oxoglutarate in Escherichia coli is tight and complex. In this work, the role of glutamine synthetase (GS) and glutamate dehydrogenase (GDH) regulation in this control was studied. Both enzymes form a linear pathway, which can also have a cyclic topology if glutamate-oxoglutarate amino transferase (GOGAT) activity is included. We modelled the metabolic pathways in the linear or cyclic topologies using a coupled nonlinear differential equations system. To simulate GS regulation by covalent modification, we introduced a relationship that took into account the levels of oxoglutarate and glutamine as signal inputs, as well as the ultrasensitive response of enzyme adenylylation. Thus, by including this relationship or not, we were able to model the system with or without GS regulation. In addition, GS and GDH activities were changed manually. The response of the model in different stationary states, or under the influence of N-input exhaustion or oscillation, was analyzed in both pathway topologies. Our results indicate a metabolic control coefficient for GDH ranging from 0.94 in the linear pathway with GS regulation to 0.24 in the cyclic pathway without regulation, employing a default GDH concentration of 8 microM. Thus, in these conditions, GDH seemed to have a high degree of control in the linear pathway while having limited influence in the cyclic one. When GS was regulated, system responses to N-input perturbations were more sensitive, especially in the cyclic pathway. Furthermore, we found that effects of regulation against perturbations depended on the relative values of the glutamine and glutamate output first-order kinetic constants, which we named k(6) and k(7), respectively. Effects of regulation grew exponentially with a factor around 2, with linear increases of (k(7) - k(6)). These trends were sustained but with lower differences at higher GS concentration. Hence, GS regulation seemed important for metabolic stability in a changing environment, depending on the cell's metabolic status.

SUBMITTER: Lodeiro A

PROVIDER: S-EPMC2577740 | biostudies-other | 2008 Apr

REPOSITORIES: biostudies-other

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Project description:Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

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Robustness in Escherichia coli glutamate and glutamine synthesis studied by a kinetic model.

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