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:mauricio.moura@ufv.br
Author ProfileWendel Batista da Silveira
Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
Author ProfileZoran 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
Author ProfileAbstract
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