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Metabolites
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Metabolomics Modelling
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Metabolomics Modelling

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 July 2017) | Viewed by 27226

Special Issue Editor

Dr. Miroslava Cuperlovic-Culf

E-Mail Website
Guest Editor
National Research Council Canada, 1200 Montreal Road, M-50 Room 353, Ottawa, ON K1A 0R6, Canada
Interests: metabolomics; metabolism modelling; computational biology; biomarker discovery; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Life on Earth depends on the dynamic transformation of chemicals—metabolites orchestrated by proteins and genes, which are, in turn, extensively regulated by metabolites. Omics data provide measurements of all of these molecules, tabulating their changes in different environments in health and disease. Obtaining knowledge of metabolism and having the ability to predict behaviors of biological systems from available data remains one the main challenges of the omics revolution. Mathematical and computational modelling analysis methods are being actively developed in order to investigate, describe and predict all steps in the metabolic process, from the interaction between molecules, all the way to the modelling of complete metabolic networks of cells and even networks of multiple cells. These efforts require major inputs from highly multidisciplinary teams aided by sophisticated computational technologies. The effort is very much worthwhile, and it has already provided a more detailed understanding of the diseases development, better determination of optimal targets for different treatments, as well as optimization of cellular growth systems such as bioreactors, to name just a few applications.

This Special Issue will focus on experimental and computational advances in the analysis of metabolic flux; modelling of metabolite regulation of protein activity and expression; metabolic processes analysis and modelling; quantitative metabolomics application in metabolism modelling; computational modelling of metabolic networks; computational analysis of the effects of genetic and nutritional modification on cell metabolism and growth. Our goal is to combine in one issue diverse examples of the application of modelling in the analysis of metabolic processes, pathways and networks, as well as their regulation.

Dr. Miroslava Cuperlovic-Culf
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

  • metabolic models
  • reconstruction of metabolic networks
  • metabolomics
  • fluxomics
  • lipidomics
  • metabolism regulation
  • system biology
  • omics data integration
  • metabolism regulation

Published Papers (4 papers)

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Research

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28 pages, 9872 KiB  
Article
Identifying Biomarkers of Wharton’s Jelly Mesenchymal Stromal Cells Using a Dynamic Metabolic Model: The Cell Passage Effect
by Benoît Laflaquière, Gabrielle Leclercq, Chandarong Choey, Jingkui Chen, Sabine Peres, Caryn Ito and Mario Jolicoeur
Metabolites 2018, 8(1), 18; https://doi.org/10.3390/metabo8010018 - 24 Feb 2018
Cited by 6 | Viewed by 4869
Abstract
Because of their unique ability to modulate the immune system, mesenchymal stromal cells (MSCs) are widely studied to develop cell therapies for detrimental immune and inflammatory disorders. However, controlling the final cell phenotype and determining immunosuppressive function following cell amplification in vitro often [...] Read more.
Because of their unique ability to modulate the immune system, mesenchymal stromal cells (MSCs) are widely studied to develop cell therapies for detrimental immune and inflammatory disorders. However, controlling the final cell phenotype and determining immunosuppressive function following cell amplification in vitro often requires prolonged cell culture assays, all of which contribute to major bottlenecks, limiting the clinical emergence of cell therapies. For instance, the multipotent Wharton’s Jelly mesenchymal stem/stromal cells (WJMSC), extracted from human umbilical cord, exhibit immunosuppressive traits under pro-inflammatory conditions, in the presence of interferon-γ (IFNγ), and tumor necrosis factor-α (TNFα). However, WJMSCs require co-culture bioassays with immune cells, which can take days, to confirm their immunomodulatory function. Therefore, the establishment of robust cell therapies would benefit from fast and reliable characterization assays. To this end, we have explored the metabolic behaviour of WJMSCs in in vitro culture, to identify biomarkers that are specific to the cell passage effect and the loss of their immunosuppressive phenotype. We clearly show distinct metabolic behaviours comparing WJMSCs at the fourth (P4) and the late ninth (P9) passages, although both P4 and P9 cells do not exhibit significant differences in their low immunosuppressive capacity. Metabolomics data were analysed using an in silico modelling platform specifically adapted to WJMSCs. Of interest, P4 cells exhibit a glycolytic metabolism compared to late passage (P9) cells, which show a phosphorylation oxidative metabolism, while P4 cells show a doubling time of 29 h representing almost half of that for P9 cells (46 h). We also clearly show that fourth passage WJMSCs still express known immunosuppressive biomarkers, although, this behaviour shows overlapping with a senescence phenotype. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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1952 KiB  
Article
Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics
by Gautham Vivek Sridharan, Bote Gosse Bruinsma, Shyam Sundhar Bale, Anandh Swaminathan, Nima Saeidi, Martin L. Yarmush and Korkut Uygun
Metabolites 2017, 7(4), 58; https://doi.org/10.3390/metabo7040058 - 13 Nov 2017
Cited by 6 | Viewed by 4292
Abstract
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight [...] Read more.
Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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1474 KiB  
Article
Effects of Storage Time on Glycolysis in Donated Human Blood Units
by Zhen Qi, John D. Roback and Eberhard O. Voit
Metabolites 2017, 7(2), 12; https://doi.org/10.3390/metabo7020012 - 29 Mar 2017
Cited by 3 | Viewed by 5069
Abstract
Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms [...] Read more.
Background: Donated blood is typically stored before transfusions. During storage, the metabolism of red blood cells changes, possibly causing storage lesions. The changes are storage time dependent and exhibit donor-specific variations. It is necessary to uncover and characterize the responsible molecular mechanisms accounting for such biochemical changes, qualitatively and quantitatively; Study Design and Methods: Based on the integration of metabolic time series data, kinetic models, and a stoichiometric model of the glycolytic pathway, a customized inference method was developed and used to quantify the dynamic changes in glycolytic fluxes during the storage of donated blood units. The method provides a proof of principle for the feasibility of inferences regarding flux characteristics from metabolomics data; Results: Several glycolytic reaction steps change substantially during storage time and vary among different fluxes and donors. The quantification of these storage time effects, which are possibly irreversible, allows for predictions of the transfusion outcome of individual blood units; Conclusion: The improved mechanistic understanding of blood storage, obtained from this computational study, may aid the identification of blood units that age quickly or more slowly during storage, and may ultimately improve transfusion management in clinics. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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Review

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16 pages, 2905 KiB  
Review
Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling
by Miroslava Cuperlovic-Culf
Metabolites 2018, 8(1), 4; https://doi.org/10.3390/metabo8010004 - 11 Jan 2018
Cited by 115 | Viewed by 12254
Abstract
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge [...] Read more.
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. Full article
(This article belongs to the Special Issue Metabolomics Modelling)
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