Special Issue "Model-Driven Data Integration of Metabolomic and Genomic Data in Microbiome Systems"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (30 December 2018).

Special Issue Editors

Prof. Bas Teusink
Guest Editor
Faculty of Science, Systems Bioinformatics, Vrije Universiteit Amsterdam
Interests: genome-scale metabolic models; constraint-based modeling; design-principles of regulation of metabolic networks
Dr. Christopher Henry
Guest Editor
The Microbiome Center, The University of Chicago
Interests: application of thermodynamics to metabolic modeling; high-throughput reconstruction of genome-scale metabolic models; computational biology

Special Issue Information

Dear Colleagues,

Increasingly, metabolomics and next generation sequencing are being used in concert to extract data from experimental systems in order to understand the biological activity occurring in these systems. Analysis of microbiome-based systems in particular require this combined approach, as sequencing or metabolomics alone are typically insufficient to fully characterize these systems. This is driving an growing need for computational techniques to analyze such data in a synergistic way, such that metabolomics data can be used to reinforce conclusions derived from sequencing data, and vice versa. It is also important that these techniques support microbiome analysis, as this is the application area where a combined use of sequencing and metabolomics is currently most essential. Genome-scale metabolic models (GEMs) have emerged as important tools to address this novel challenge: (i) GEMs serve as a bridge between genomics and chemistry containing interconnected representation of both data types; (ii) GEMs have a distinct capacity to integrate numerous individual data points together into a broader representation of biological activity; and (iii) GEMs may be extended into broader models of entire microbial communities. However, numerous challenges continue to hinder efforts to apply GEMs to seamlessly integrate metabolomics and genomic data in microbiome systems. First and foremost, we have a significant dark-matter problem hindering both metabolomics data annotation and genomic data annotation. Numerous peaks observed in metabolomics data cannot be identified, and numerous proteins observed in genomic data cannot be annotated. GEM modeling must be augmented by new approaches that facilitate peak identification and rapidly propose new pathways to fill gaps in our biochemical knowledgebase. Second, we need new simulation and analysis techniques for applying GEM modeling to integrate metabolomic and genomic data in microbiome systems. Finally, we need improved and extensible biochemistry databases to provide a more complete set of candidates for metabolomics peak identification and also to facilitate GEM interoperability in community modeling.

Such databases must facilitate rapid extension to accommodate newly discovered compounds, reactions, pathways, and protein functions. This Special Issue of Metabolomics is focused on genome-scale metabolic modeling, and how genome-scale metabolic models are used to integrate metabolomics and next generation sequencing data for analysis of isolate and microbiome-based systems. We are particularly interested in articles about: (i) improved annotation of metabolomic and genomic data by removing knowledge gaps in microbiome system chemistry; (ii) new metabolomics and genomic data integration techniques using GEMs in microbiome systems; and (iii) improved databases that support improved annotation of metabolomic data, model reconstruction, and model interoperability.

Prof. Bas Teusink
Dr. Christopher Henry
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 papers will be 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 1600 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.


  • Genome-scale metabolic model
  • Metabolomics
  • Data integration
  • Metabolic interactions

Published Papers (1 paper)

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Open AccessArticle
Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling
Metabolites 2019, 9(2), 28; https://doi.org/10.3390/metabo9020028 - 06 Feb 2019
Cited by 5
Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit [...] Read more.
Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping. Full article
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