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Grigaitis P, Olivier BG, Fiedler T, et al. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2020;327:54-63doi: 10.1016/j.jbiotec.2020.11.003.
Grigaitis, P., Olivier, B. G., Fiedler, T., Teusink, B., Kummer, U., & Veith, N. (2021). Protein cost allocation explains metabolic strategies in Escherichia coli. Journal of biotechnology, 32754-63. https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis, Pranas, et al. "Protein cost allocation explains metabolic strategies in Escherichia coli." Journal of biotechnology vol. 327 (2021): 54-63. doi: https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10. PMID: 33309962.
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J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10.

Protein cost allocation explains metabolic strategies in Escherichia coli.

Journal of biotechnology

Pranas Grigaitis, Brett G Olivier, Tomas Fiedler, Bas Teusink, Ursula Kummer, Nadine Veith

Affiliations

  1. Modeling of Biological Processes, BioQuant/Center for Organismal Studies Heidelberg, Heidelberg University, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany; Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU Amsterdam, De Boelelaan 1085, NL-1081HZ Amsterdam, The Netherlands.
  2. Institute of Medical Microbiology, Virology, and Hygiene, Rostock University Medical Center, Schillingallee 70, D-18055 Rostock, Germany.
  3. Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU Amsterdam, De Boelelaan 1085, NL-1081HZ Amsterdam, The Netherlands.
  4. Modeling of Biological Processes, BioQuant/Center for Organismal Studies Heidelberg, Heidelberg University, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany.
  5. Modeling of Biological Processes, BioQuant/Center for Organismal Studies Heidelberg, Heidelberg University, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany. Electronic address: [email protected].

PMID: 33309962 DOI: 10.1016/j.jbiotec.2020.11.003

Abstract

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Keywords: Genome-scale models; Microbial metabolism; Quantitative proteomics; Resource allocation

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