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 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.
Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 37673, Republic of Korea.
Jaeyun
J
Sung
Center for Individualized Medicine, Microbiome Program, Mayo Clinic, Rochester, MN 55905, USA; Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.
Vanessa
V
Hale
Center for Individualized Medicine, Microbiome Program, Mayo Clinic, Rochester, MN 55905, USA.
Annette C
AC
Merkel
Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk 37673, Republic of Korea; Department of Physics, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Republic of Korea.
Pan-Jun
PJ
Kim
Center for Individualized Medicine, Microbiome Program, Mayo Clinic, Rochester, MN 55905, USA; Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA; Department of Biomedical Engineering, Mayo College, Rochester, MN 55905, USA.
Nicholas
N
Chia
Big Data; COMM, community-scale metabolic modeling; Data integration; GEMs, genome scale metabolic models; Gut microbiome; HMP, Human Microbiome Project; Metabolic modeling; Microbial community
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