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Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbi...

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Detalles Bibliográficos
Autores principales: Sung, Jaeyun, Hale, Vanessa, Merkel, Annette C., Kim, Pan-Jun, Chia, Nicholas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025471/
https://www.ncbi.nlm.nih.gov/pubmed/27668170
http://dx.doi.org/10.1016/j.atg.2016.02.001
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author Sung, Jaeyun
Hale, Vanessa
Merkel, Annette C.
Kim, Pan-Jun
Chia, Nicholas
author_facet Sung, Jaeyun
Hale, Vanessa
Merkel, Annette C.
Kim, Pan-Jun
Chia, Nicholas
author_sort Sung, Jaeyun
collection PubMed
description The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework – or metabolic model – on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.
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spelling pubmed-50254712016-09-23 Metabolic modeling with Big Data and the gut microbiome Sung, Jaeyun Hale, Vanessa Merkel, Annette C. Kim, Pan-Jun Chia, Nicholas Appl Transl Genom Article The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework – or metabolic model – on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling. Elsevier 2016-02-05 /pmc/articles/PMC5025471/ /pubmed/27668170 http://dx.doi.org/10.1016/j.atg.2016.02.001 Text en © 2016 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Sung, Jaeyun
Hale, Vanessa
Merkel, Annette C.
Kim, Pan-Jun
Chia, Nicholas
Metabolic modeling with Big Data and the gut microbiome
title Metabolic modeling with Big Data and the gut microbiome
title_full Metabolic modeling with Big Data and the gut microbiome
title_fullStr Metabolic modeling with Big Data and the gut microbiome
title_full_unstemmed Metabolic modeling with Big Data and the gut microbiome
title_short Metabolic modeling with Big Data and the gut microbiome
title_sort metabolic modeling with big data and the gut microbiome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5025471/
https://www.ncbi.nlm.nih.gov/pubmed/27668170
http://dx.doi.org/10.1016/j.atg.2016.02.001
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