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:	Nicolai S. Panikov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	growth kinetics survival death substrate limitation starvation gene expression Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/fd4e41c8b5ae486da7685dc7683d79ac
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spelling	oai:doaj.org-article:fd4e41c8b5ae486da7685dc7683d79ac2021-11-25T18:25:21ZGenome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges10.3390/microorganisms91123522076-2607https://doaj.org/article/fd4e41c8b5ae486da7685dc7683d79ac2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-2607/9/11/2352https://doaj.org/toc/2076-2607This 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advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances.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.Nicolai S. PanikovMDPI AGarticlegrowth kineticssurvivaldeathsubstrate limitationstarvationgene expressionBiology (General)QH301-705.5ENMicroorganisms, Vol 9, Iss 2352, p 2352 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	growth kinetics survival death substrate limitation starvation gene expression Biology (General) QH301-705.5
spellingShingle	growth kinetics survival death substrate limitation starvation gene expression Biology (General) QH301-705.5 Nicolai S. Panikov Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
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.
format	article
author	Nicolai S. Panikov
author_facet	Nicolai S. Panikov
author_sort	Nicolai S. Panikov
title	Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges
title_short	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_sort	genome-scale reconstruction of microbial dynamic phenotype: successes and challenges
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fd4e41c8b5ae486da7685dc7683d79ac
work_keys_str_mv	AT nicolaispanikov genomescalereconstructionofmicrobialdynamicphenotypesuccessesandchallenges
_version_	1718411237335760896

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

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