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Son JW, Shoaie S, Lee S. Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome. Endocrinol Metab (Seoul). 2020;35(3):507-514doi: 10.3803/EnM.2020.303.
Son, J. W., Shoaie, S., & Lee, S. (2020). Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome. Endocrinology and metabolism (Seoul, Korea), 35(3), 507-514. https://doi.org/10.3803/EnM.2020.303
Son, Jang Won, et al. "Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome." Endocrinology and metabolism (Seoul, Korea) vol. 35,3 (2020): 507-514. doi: https://doi.org/10.3803/EnM.2020.303
Son JW, Shoaie S, Lee S. Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome. Endocrinol Metab (Seoul). 2020 Sep;35(3):507-514. doi: 10.3803/EnM.2020.303. Epub 2020 Sep 22. PMID: 32981293; PMCID: PMC7520591.
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Endocrinol Metab (Seoul). 2020 Sep;35(3):507-514. doi: 10.3803/EnM.2020.303. Epub 2020 Sep 22.

Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome.

Endocrinology and metabolism (Seoul, Korea)

Jang Won Son, Saeed Shoaie, Sunjae Lee

Affiliations

  1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea.
  2. Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
  3. Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.

PMID: 32981293 PMCID: PMC7520591 DOI: 10.3803/EnM.2020.303

Abstract

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

Keywords: Gastrointestinal microbiome; Metabolism; Systems biology

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