388925 A Kinetic Model of Escherichia coli Core Metabolism Satisfying Multiple Sets of Mutant Flux Data
In contrast to stoichiometric-based models, the development of large-scale kinetic models of metabolism has been hindered by the challenge of identifying kinetic parameter values and kinetic rate laws applicable to a wide range of environmental and/or genetic perturbations. The recently introduced ensemble modeling (EM) procedure was developed towards achieving this goal through successively reducing the parameter space by using phenotypic data such as experimental flux measurements and decomposing the reactions into the elementary mechanism. Here, we developed an optimization-driven parameter estimation method and combined it with EM approach to construct a large-scale kinetic models of E. coli metabolism satisfying the fluxomic data for wild-type and seven mutant strains. This model encompasses 138 reactions and 93 metabolites accounting for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions, and a number of reactions in other parts of the metabolism and accounts for 60 regulatory reactions that are mainly included the central metabolism interactions. Parameterization is performed using a formal optimization approach that minimizes the discrepancies between model predictions and flux measurements. The predicted fluxes by the model are within the uncertainty range of experimental flux data for 78% of the reactions (with measured fluxes) for both the reference (wild-type) and seven mutant strains. The remaining flux predictions fall within three standard deviations of measured values. Converting the EM-based parameters into a Michaelis-Menten equivalent formalism revealed that 80% of Km and kcat parameters are within one order of magnitude of literature available values. The predicted metabolite concentrations by the model are also within uncertainty ranges of metabolomic data for 68% of the metabolites. A leave-one-out cross-validation test to evaluate the flux prediction performance of the model showed that metabolic fluxes for the mutants located in the proximity of mutations used for training the model are predicted more accurately.
The constructed model and parameterization procedure provides the means for the construction of both larger models and models with more narrowly distributed parameter values as new metabolomics/fluxomic data sets are becoming available for E. coli and other well studied organisms.
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