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Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life be...

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Autor principal: Panikov, Nicolai S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621822/
https://www.ncbi.nlm.nih.gov/pubmed/34835477
http://dx.doi.org/10.3390/microorganisms9112352
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author Panikov, Nicolai S.
author_facet Panikov, Nicolai S.
author_sort Panikov, Nicolai S.
collection PubMed
description This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.
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spelling pubmed-86218222021-11-27 Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges Panikov, Nicolai S. Microorganisms Review This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition. MDPI 2021-11-14 /pmc/articles/PMC8621822/ /pubmed/34835477 http://dx.doi.org/10.3390/microorganisms9112352 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Panikov, Nicolai S.
Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_full Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_fullStr Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_full_unstemmed Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_short Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_sort genome-scale reconstruction of microbial dynamic phenotype: successes and challenges
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621822/
https://www.ncbi.nlm.nih.gov/pubmed/34835477
http://dx.doi.org/10.3390/microorganisms9112352
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