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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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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. |
format | Online Article Text |
id | pubmed-5025471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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