Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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Author to whom correspondence should be addressed.
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Authors contributed equally.
Metabolites 2020, 10(8), 303; https://doi.org/10.3390/metabo10080303
Received: 31 May 2020
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Revised: 8 July 2020
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Accepted: 22 July 2020
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Published: 24 July 2020
(This article belongs to the Special Issue Animal and Cellular Models in Metabolomics Research)
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Keywords:
metabolic modelling; data integration; metabolomics
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MDPI and ACS Style
Volkova, S.; Matos, M.R.A.; Mattanovich, M.; Marín de Mas, I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020, 10, 303. https://doi.org/10.3390/metabo10080303
AMA Style
Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites. 2020; 10(8):303. https://doi.org/10.3390/metabo10080303
Chicago/Turabian StyleVolkova, Svetlana, Marta R.A. Matos, Matthias Mattanovich, and Igor Marín de Mas. 2020. "Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis" Metabolites 10, no. 8: 303. https://doi.org/10.3390/metabo10080303
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