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Protein constraints in genome-scale metabolic models: data integration, parameter estimation, and prediction of metabolic phenotypes
  • Maurício Alexander de Moura Ferreira,
  • Wendel Batista da Silveira,
  • Zoran Nikoloski
Maurício Alexander de Moura Ferreira
Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil

Corresponding Author:[email protected]

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Wendel Batista da Silveira
Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
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Zoran Nikoloski
Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany, Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
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Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modelling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Lastly, we identify standing challenges in protein-constraint metabolic models and provide a perspective regarding future approaches to improve the predictive performance.
18 Aug 2022Submitted to Biotechnology and Bioengineering
18 Aug 2022Submission Checks Completed
18 Aug 2022Assigned to Editor
02 Sep 2022Reviewer(s) Assigned
27 Jul 2023Review(s) Completed, Editorial Evaluation Pending
24 Sep 2023Editorial Decision: Revise Major
25 Oct 20231st Revision Received
26 Oct 2023Submission Checks Completed
26 Oct 2023Assigned to Editor
26 Oct 2023Review(s) Completed, Editorial Evaluation Pending