Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

Chung, Wan Yean; Zhu, Yan; Maifiah, Mohd Hafidz Mahamad; Shivashekaregowda, Naveen Kumar Hawala; Wong, Eng Hwa; Rahim, Nusaibah Abdul

doi:10.1038/s41429-020-00366-2

Cited by 10 publications

(6 citation statements)

References 72 publications

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“…Comparing metabolic networks to find commonalities can facilitate the discovery of potential broadly-distributed targets, such as for developing new antimicrobial drugs [49]. To illustrate this application, we used mergem in Fluxer to visualize the commonalities between the curated GEMs for three gram-negative pathogens: Acinetobacter baumanii [50] , Klebsiella pneumoniae [51], and Pseudomonas aeruginosa [48].…”

Section: Resultsmentioning

confidence: 99%

mergem: merging and comparing genome-scale metabolic models using universal identifiers

Hari

Lobo

2022

Preprint

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Numerous methods exist to produce and refine draft genome-scale metabolic models. However, there is a lack of automated tools that can integrate these drafts into a curated single comprehensive model. In addition, computing and visualizing metabolic differences and similarities, including reconstructions using incompatible identifiers, is a current challenge. Here we present mergem, a novel method to compare and merge two or more metabolic models using a universal metabolic identifier mapping system constructed from multiple metabolic databases. mergem is implemented as a Python package and on the web-application Fluxer, which allows simulating and comparing multiple models with different interactive flux graphs.

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Section: Resultsmentioning

confidence: 99%

mergem: merging and comparing genome-scale metabolic models using universal identifiers

Hari

Lobo

2022

Preprint

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“…The resultant metabolic status indicated that the highest hexanoic acid production could be yielded when the balanced fractional contribution via acetyl-CoA and succinate in the reductive TCA cycle was formed in various cell growth rates. Chung et al reviewed the development of GSMMs for Gram-negative pathogens and metabolic network approaches for identifying antimicrobial drug targets in 2021 [19]. Guzmán et al proposed seven isozyme functions based on genetic and transcriptional evidence using genome-scale models in 2015 [20].…”

Section: The Development Of Gsmmsmentioning

confidence: 99%

Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

Bai¹,

You²,

Zhang

et al. 2022

Syst Microbiol and Biomanuf

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Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

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“…Since its launch in 2016, merlin's website was visited more than eleven thousand times and over three thousand downloads were performed. Merlin has gained notoriety over the years and is nowadays recognised as a reference software for the reconstruction of high-quality GSMM by the scientific community through multiple research articles (54)(55)(56)(57)(58)(59)(60)(61)(62), reviews, and book chapters (1,4,(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76)(77)(78)(79)(80).…”

Section: Validationmentioning

confidence: 99%

merlin v4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

Capela

Lagoa

Rodrigues

et al. 2021

Preprint

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Genome-scale metabolic models have been recognized as useful tools for better understanding living organism's metabolism. Merlin (https://merlin-sysbio.org/) is an open-source and user-friendly resource that hastens these models' reconstruction process, conjugating manual, and automatic procedures, while leveraging user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features were implemented in merlin, along with profound changes in the software architecture, operating flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates led to an increase in the user-base, resulting in multiple published works including genome metabolic (re-)annotation and model reconstruction of multiple (lower and higher) eukaryotes and prokaryotes.

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Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

Cited by 10 publications

References 72 publications

mergem: merging and comparing genome-scale metabolic models using universal identifiers

mergem: merging and comparing genome-scale metabolic models using universal identifiers

Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models

merlin v4.0: an updated platform for the reconstruction of high-quality genome-scale metabolic models

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