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Metabolites
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Metabolic Networks
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Metabolic Networks

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 January 2020) | Viewed by 11395

Special Issue Editor

Dr. Fabien Jourdan

E-Mail Website
Guest Editor
INRA, Toulouse, France
Interests: metabolic networks; bioinformatics; visualisation; metabolomics; graphs

Special Issue Information

Dear Colleagues,

Thanks to last year’s technological and methodological developments, metabolomics now allows us to detect and identify tens to hundreds of metabolites constituting metabolic fingerprints of environmental or genetic stresses. These metabolic profiles provide highly valuable information on the physiological status of cells, tissues or organisms. The current challenge lies in putting back these lists of metabolites in the context of genome scale metabolic networks. In fact, metabolic networks aim at gathering all the metabolic reactions which can occur in a given organism, hence allowing us to decipher the cascade of reactions connecting metabolites identified in metabolomics.

This Special Issue is devoted to reviewing the current practical aspects of metabolomic data analysis in the context of metabolic networks, starting from network reconstruction, metabolite mapping (with a special challenge in identifier mappings), then discussing state-of-the-art and new network computational analysis methods (graphs, constraint-based modelling) and emphasising the need for new visualisation paradigms to deal with these large networks. We also invite reviews and viewpoints which could help in filling the gap between network modelling and metabolomics data analysis. Finally, we also invite manuscripts showing the use of metabolic networks in the challenging task of metabolite identification.

Dr. Fabien Jourdan
Guest Editor

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 networks
  • Metabolomics data network mapping
  • Network reconstruction
  • Network visualisation

Published Papers (3 papers)

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Research

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20 pages, 3667 KiB  
Article
Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content
by Ahmad Ahmad, Ruchi Pathania and Shireesh Srivastava
Metabolites 2020, 10(5), 177; https://doi.org/10.3390/metabo10050177 - 29 Apr 2020
Cited by 9 | Viewed by 3017
Abstract
Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 [...] Read more.
Marine cyanobacteria are promising microbes to capture and convert atmospheric CO2 and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds. Full article
(This article belongs to the Special Issue Metabolic Networks)
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11 pages, 1189 KiB  
Article
Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model
by Michael Witting
Metabolites 2020, 10(4), 130; https://doi.org/10.3390/metabo10040130 - 28 Mar 2020
Cited by 3 | Viewed by 2495
Abstract
Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic [...] Read more.
Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic networks in silico or used for analysis of omics data. Beside correct mass and charge balance, correct structural annotation of metabolites represents an important factor for analysis of these metabolic networks. However, several metabolites in different GSMs have no or only partial structural information associated with them. Here, a new systematic nomenclature for acyl-based metabolites such as fatty acids, acyl-carnitines, acyl-coenzymes A or acyl-carrier proteins is presented. This nomenclature enables one to encode structural details in the metabolite identifiers and improves human readability of reactions. As proof of principle, it was applied to the fatty acid biosynthesis and degradation in the Caenorhabditis elegans consensus model WormJam. Full article
(This article belongs to the Special Issue Metabolic Networks)
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14 pages, 740 KiB  
Protocol
EFMviz: A COBRA Toolbox Extension to Visualize Elementary Flux Modes in Genome-Scale Metabolic Models
by Chaitra Sarathy, Martina Kutmon, Michael Lenz, Michiel E. Adriaens, Chris T. Evelo and Ilja C.W. Arts
Metabolites 2020, 10(2), 66; https://doi.org/10.3390/metabo10020066 - 12 Feb 2020
Cited by 5 | Viewed by 5535
Abstract
Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA [...] Read more.
Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study. Full article
(This article belongs to the Special Issue Metabolic Networks)
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