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Metabolites
Special Issues
Reconstruction of Genome-Scale Metabolic Models
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Reconstruction of Genome-Scale Metabolic Models

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6393

Special Issue Editors

Dr. Miguel Rocha

E-Mail Website
Guest Editor
Centre of Biological Engineering/ Department of Informatics, University of Minho, 4710-057 Braga, Portugal
Interests: machine and deep learning; evolutionary computation; bioinformatics; systems biology; constraint-based modeling; metabolomics
Special Issues, Collections and Topics in MDPI journals
Dr. Oscar Dias

E-Mail Website
Guest Editor
Centre of Biological Engineering/ Department of Biological Engineering, University of Minho, 4710-057 Braga, Portugal
Interests: systems biology; bioinformatics; constraint-based modeling; metabolic models reconstruction; dynamic modeling; machine learning

Special Issue Information

Dear Colleagues,

The organism’s phenotype reflects the outcome of the mechanisms governing the cell’s operations, which may be affected by biotic and abiotic stressors. Genome-scale metabolic models (GSMs) are a systems biology tool, commonly used in different fields of science today, from metabolic engineering in biotechnology to biomedical applications such as antibiotic discovery, metabolic disorders, or cancer research, but also addressing topics in fields such as wastewater treatment and food technology. GSMs allow understanding the organism’s metabolic response to changes in the underlying environment, but also studying phenotypes arising from genetic mutations.

In this Special Issue, we welcome submissions of manuscripts related to the reconstruction of genome-scale metabolic models and their applications. Topics that this Special Issue will cover include but are not limited to reconstruction of genome-scale metabolic models, applications of genome-scale metabolic models, combining machine learning with GSMs, novel approaches for integrating omics data with models, model and pathway visualization, and novel software tools or databases in these topics.

Manuscripts with an impact on systems biology and bioinformatics from both software developers and researchers developing and applying existing methods and computational tools to genome-scale metabolic model reconstruction and applications are welcome.

Dr. Miguel Rocha
Dr. Oscar Dias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genome-scale metabolic models
  • constraint-based modeling
  • systems biology
  • machine learning
  • open-source software tools
  • Omics data
  • metabolism
  • pathway visualization

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Published Papers (2 papers)

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Research

14 pages, 1711 KiB  
Article
A Genome-Scale Metabolic Model of Methanoperedens nitroreducens: Assessing Bioenergetics and Thermodynamic Feasibility
by Bingqing He, Chen Cai, Tim McCubbin, Jorge Carrasco Muriel, Nikolaus Sonnenschein, Shihu Hu, Zhiguo Yuan and Esteban Marcellin
Metabolites 2022, 12(4), 314; https://doi.org/10.3390/metabo12040314 - 31 Mar 2022
Cited by 4 | Viewed by 2796
Abstract
Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) [...] Read more.
Methane is an abundant low-carbon fuel that provides a valuable energy resource, but it is also a potent greenhouse gas. Therefore, anaerobic oxidation of methane (AOM) is an essential process with central features in controlling the carbon cycle. Candidatus ‘Methanoperedens nitroreducens’ (M. nitroreducens) is a recently discovered methanotrophic archaeon capable of performing AOM via a reverse methanogenesis pathway utilizing nitrate as the terminal electron acceptor. Recently, reverse methanogenic pathways and energy metabolism among anaerobic methane-oxidizing archaea (ANME) have gained significant interest. However, the energetics and the mechanism for electron transport in nitrate-dependent AOM performed by M. nitroreducens is unclear. This paper presents a genome-scale metabolic model of M. nitroreducens, iMN22HE, which contains 813 reactions and 684 metabolites. The model describes its cellular metabolism and can quantitatively predict its growth phenotypes. The essentiality of the cytoplasmic heterodisulfide reductase HdrABC in the reverse methanogenesis pathway is examined by modeling the electron transfer direction and the specific energy-coupling mechanism. Furthermore, based on better understanding electron transport by modeling, a new energy transfer mechanism is suggested. The new mechanism involves reactions capable of driving the endergonic reactions in nitrate-dependent AOM, including the step reactions in reverse canonical methanogenesis and the novel electron-confurcating reaction HdrABC. The genome metabolic model not only provides an in silico tool for understanding the fundamental metabolism of ANME but also helps to better understand the reverse methanogenesis energetics and its thermodynamic feasibility. Full article
(This article belongs to the Special Issue Reconstruction of Genome-Scale Metabolic Models)
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12 pages, 939 KiB  
Article
Network Reconstruction and Modelling Made Reproducible with moped
by Nima P. Saadat, Marvin van Aalst and Oliver Ebenhöh
Metabolites 2022, 12(4), 275; https://doi.org/10.3390/metabo12040275 - 22 Mar 2022
Cited by 5 | Viewed by 2646
Abstract
Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well [...] Read more.
Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses. Full article
(This article belongs to the Special Issue Reconstruction of Genome-Scale Metabolic Models)
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