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Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

David Heckmann, Anaamika Campeau, Colton J. Lloyd, Patrick V. Phaneuf, Ying Hefner, Marvic Carrillo-Terrazas, Adam M. Feist, David J. Gonzalez, Bernhard Ø. Palsson

2020Proceedings of the National Academy of Sciences91 citationsDOIOpen Access PDF

Abstract

Significance Enzyme kinetic parameters are crucial for a quantitative understanding of metabolism, but traditionally have to be measured in laborious low-throughput assays. To solve this problem, the enzyme turnover number, k cat , can be estimated in vivo, but it is unclear whether in vivo estimates represent stable systems parameters that can be used for metabolic modeling. We present the data-driven estimation of in vivo k cat s using proteomics and flux data of metabolic knock out strains of Escherichia coli . Our results show that in vivo k cat s are stable parameters that can be used for metabolic modeling. We use the estimated in vivo k cat s to parameterize metabolic models and show that model performance for gene expression predictions increases drastically compared to in vitro parameters.

Topics & Concepts

In vivoConsistency (knowledge bases)EnzymeProfiling (computer programming)Computational biologyChemistryBiochemistryBiologyMathematicsComputer scienceGeneticsDiscrete mathematicsOperating systemMicrobial Metabolic Engineering and BioproductionEnzyme Catalysis and ImmobilizationMitochondrial Function and Pathology
Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers | Litcius