Metabolic modeling with Big Data and the gut microbiome

Jaeyun Sung, Vanessa Hale, Annette C. Merkel, Pan Jun Kim, Nicholas D Chia

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
JournalApplied and Translational Genomics
DOIs
StateAccepted/In press - Aug 10 2015

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Microbiota
Metabolic Networks and Pathways
Ecology
Biomarkers
Research Personnel
Technology
Research
Gastrointestinal Microbiome

Keywords

  • Big Data
  • Data integration
  • Gut microbiome
  • Metabolic modeling
  • Microbial community

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Pharmaceutical Science

Cite this

Metabolic modeling with Big Data and the gut microbiome. / Sung, Jaeyun; Hale, Vanessa; Merkel, Annette C.; Kim, Pan Jun; Chia, Nicholas D.

In: Applied and Translational Genomics, 10.08.2015.

Research output: Contribution to journalArticle

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