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Dear Colleagues,

Over the past decade and a half, bioinformatics has evolved from the role of being a support science to now becoming an integral part of all areas of metabolomics. Bioinformatic-defined models and solutions are part of the entire workflow of metabolomics, starting from methodically defining and capturing an experiment using standards, managing the vast amount of high-throughput data arising from these experiments, making sense of these data using data processing and statistical analysis methods, and finally relating the findings back to the underlying question using data integration, pathway analysis and visualization models. This Special Issue focuses on bioinformatics methods, tools, systems and solutions in metabolomics. Specific areas include, but not limited to, data standards, data management solution, database systems, data processing and statistical methods, pathway analysis and visualization techniques.

Dr. Thusitha W. Rupasinghe
Guest Editors

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Keywords

metabolomics bioinformatics
data processing
statistical analysis
data management
database systems
data visualization

Published Papers (2 papers)

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Research

Open AccessArticle

The Fate of Glutamine in Human Metabolism. The Interplay with Glucose in Proliferating Cells

Jean-Pierre Mazat

and

Stéphane Ransac

Metabolites 2019, 9(5), 81; https://doi.org/10.3390/metabo9050081 - 26 Apr 2019

Cited by 18 | Viewed by 3763

Abstract

Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites, and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model oxidative phosphorylation (OXPHOS) functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudometabolites DPH (∆pH) and DPSI (∆ψ). To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used flux balance analysis (FBA) to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect. Full article

(This article belongs to the Special Issue Bioinformatics in Metabolomics)

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Open AccessArticle

and

Metabolites 2019, 9(3), 51; https://doi.org/10.3390/metabo9030051 - 15 Mar 2019

Cited by 18 | Viewed by 2818

Abstract

Metabolomics is the systematic study of the small-molecule profiles of biological samples produced by specific cellular processes. The high-throughput technologies used in metabolomic investigations generate datasets where variables are strongly correlated and redundancy is present in the data. Discovering the hidden information is a challenge, and suitable approaches for data analysis must be employed. Projection to latent structures regression (PLS) has successfully solved a large number of problems, from multivariate calibration to classification, becoming a basic tool of metabolomics. PLS2 is the most used implementation of PLS. Despite its success, PLS2 showed some limitations when the so called ‘structured noise’ affects the data. Suitable methods have been recently introduced to patch up these limitations. In this study, a comprehensive and up-to-date presentation of PLS2 focused on metabolomics is provided. After a brief discussion of the mathematical framework of PLS2, the post-transformation procedure is introduced as a basic tool for model interpretation. Orthogonally-constrained PLS2 is presented as strategy to include constraints in the model according to the experimental design. Two experimental datasets are investigated to show how PLS2 and its improvements work in practice. Full article

(This article belongs to the Special Issue Bioinformatics in Metabolomics)

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