%0 Journal Article %1 Khodayari2014Kinetic %A Khodayari, Ali %A Zomorrodi, Ali R. %A Liao, James C. %A Maranas, Costas D. %D 2014 %J Metabolic engineering %K central-carbon-metabolism ecoli flux-analysis kinetic-modelling %R 10.1016/j.ymben.2014.05.014 %T A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. %U http://dx.doi.org/10.1016/j.ymben.2014.05.014 %X 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 provides a promising remedy by decomposing metabolic reactions into elementary reaction steps thereby bypassing the need for detailed kinetic expressions for all reactions. Here, we report on a kinetic model of E. coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the EM concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions 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. 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 are mostly within three standard deviations of measured ranges. 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-reported 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 can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms. Copyright \copyright 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

@article{Khodayari2014Kinetic, abstract = {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 provides a promising remedy by decomposing metabolic reactions into elementary reaction steps thereby bypassing the need for detailed kinetic expressions for all reactions. Here, we report on a kinetic model of E. coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the {EM} concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions 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. 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 are mostly within three standard deviations of measured ranges. 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-reported 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 can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms. Copyright {\copyright} 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Khodayari, Ali and Zomorrodi, Ali R. and Liao, James C. and Maranas, Costas D.}, biburl = {https://www.bibsonomy.org/bibtex/2905692b031c9a32b275c8c49b2e4218e/karthikraman}, citeulike-article-id = {13227517}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ymben.2014.05.014}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/24928774}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=24928774}, day = 10, doi = {10.1016/j.ymben.2014.05.014}, interhash = {47ff3dbc6dadf42d74b1d3a2074eb7ae}, intrahash = {905692b031c9a32b275c8c49b2e4218e}, issn = {1096-7184}, journal = {Metabolic engineering}, keywords = {central-carbon-metabolism ecoli flux-analysis kinetic-modelling}, month = jun, pmid = {24928774}, posted-at = {2014-06-16 06:49:43}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data.}, url = {http://dx.doi.org/10.1016/j.ymben.2014.05.014}, year = 2014 }