On correlated reaction sets and coupled reaction sets in metabolic networks

Marashi, Sayed-Amir; Hosseini, Zhaleh

doi:10.1142/s0219720015710031

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…In a metabolic model, reactions with linearly correlated fluxes form the correlated reaction set (Co-set) (Marashi & Hosseini, 2015; Price, Schellenberger & Palsson, 2004). Earlier studies on reaction correlation showed that uniformly sampled fluxes can be used to explore the regulatory properties of reactions in metabolic models (Barrett, Price & Palsson, 2006; Barrett, Herrgard & Palsson, 2009).…”

Section: Resultsmentioning

confidence: 99%

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

Gupta

Ahmad

Chothwe

et al. 2019

PeerJ

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The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

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Section: Resultsmentioning

confidence: 99%

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

Gupta

Ahmad

Chothwe

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…In a metabolic model, reactions with linearly correlated fluxes form the "correlated reaction set" (Co-set) (Marashi & Hosseini 2015;Price et al 2004). Earlier studies on reaction correlation showed that uniformly sampled fluxes can be used to explore the regulatory properties of reactions in metabolic models (Barrett et al 2009;Barrett et al 2006).…”

Section: Network Topology and Reaction Subsetsmentioning

confidence: 99%

Peer Review #1 of "Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph Methylococcus capsulatus str. Bath (v0.1)"

Ravcheev¹

2019

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The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner-Duodoroff (ED) pathway to be essential for the growth of this bacterium, whereas the Embden-Meyerhof-Parnas (EMP) pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis (FBA), 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a genome-scale metabolic model of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

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“…435set" (Co-set)(Price et al, 2004;Marashi and Hosseini, 2015). Earlier studies on reaction 436 correlation showed that uniformly sampled fluxes can be used to explore the regulatory437 properties of reactions in metabolic models (Barrett et al, 2006;Barrett et al, 2009).…”

mentioning

confidence: 99%

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

Gupta

Ahmad

Chothwe

et al. 2018

Preprint

View full text Add to dashboard Cite

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 898 reactions and 784 unique metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner-Duodoroff (ED) pathway to be essential for the growth of this bacterium, whereas the Embden-Meyerhof-Parnas (EMP) pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis (FBA), 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a genome-scale metabolic model of a unique methanotroph Methylococcus capsulatus str. Bath. The model will serve as a knowledge-base for deeper understanding, as a platform for exploring the metabolic potential, and as a tool to engineer this bacterium for methane mitigation and industrial applications.

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On correlated reaction sets and coupled reaction sets in metabolic networks

Cited by 3 publications

References 36 publications

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

Peer Review #1 of "Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph Methylococcus capsulatus str. Bath (v0.1)"

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

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