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State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. / Куляшов, Михаил Андреевич; Колмыков, Семён Константинович; Хлебодарова, Тамара Михайловна et al.

In: Microorganisms, Vol. 11, No. 12, 2987, 14.12.2023.

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Куляшов МА, Колмыков СК, Хлебодарова ТМ, Акбердин ИР. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms. 2023 Dec 14;11(12):2987. doi: 10.3390/microorganisms11122987

Author

Куляшов, Михаил Андреевич ; Колмыков, Семён Константинович ; Хлебодарова, Тамара Михайловна et al. / State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. In: Microorganisms. 2023 ; Vol. 11, No. 12.

BibTeX

@article{f06ab82b06f24995b59137bceaa0062e,
title = "State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs",
abstract = "Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.",
keywords = "constraint-based modeling, context-specific modeling, genome-scale metabolic modeling, methanotrophy, pipeline, tool, transcriptomics",
author = "Куляшов, {Михаил Андреевич} and Колмыков, {Семён Константинович} and Хлебодарова, {Тамара Михайловна} and Акбердин, {Илья Ринатович}",
note = "The study was financially supported by the Russian Science Foundation (project № 23-24-00606, https://rscf.ru/en/project/23-24-00606/).",
year = "2023",
month = dec,
day = "14",
doi = "10.3390/microorganisms11122987",
language = "English",
volume = "11",
journal = "Microorganisms",
issn = "2076-2607",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "12",

}

RIS

TY - JOUR

T1 - State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs

AU - Куляшов, Михаил Андреевич

AU - Колмыков, Семён Константинович

AU - Хлебодарова, Тамара Михайловна

AU - Акбердин, Илья Ринатович

N1 - The study was financially supported by the Russian Science Foundation (project № 23-24-00606, https://rscf.ru/en/project/23-24-00606/).

PY - 2023/12/14

Y1 - 2023/12/14

N2 - Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.

AB - Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.

KW - constraint-based modeling

KW - context-specific modeling

KW - genome-scale metabolic modeling

KW - methanotrophy

KW - pipeline

KW - tool

KW - transcriptomics

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85180675040&origin=inward&txGid=271955e8894959b28240ab3d93f21c39

UR - https://www.mendeley.com/catalogue/a8f5de38-c475-35e5-a64a-e12f17dbada2/

U2 - 10.3390/microorganisms11122987

DO - 10.3390/microorganisms11122987

M3 - Review article

C2 - 38138131

VL - 11

JO - Microorganisms

JF - Microorganisms

SN - 2076-2607

IS - 12

M1 - 2987

ER -

ID: 59344701