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Computational Systems Biology pp 265–292Cite as

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1754))

Abstract

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

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  1. Jing Cheng and Wenxian Lan contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Instrument, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Jing Cheng

  2. State Key Laboratory of Bio-Organic and Natural Product Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China

    Wenxian Lan

  3. Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Guangyong Zheng

  4. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Xianfu Gao

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  1. Jing Cheng
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  2. Wenxian Lan
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  3. Guangyong Zheng
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  4. Xianfu Gao
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Correspondence to Guangyong Zheng or Xianfu Gao .

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  1. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Cheng, J., Lan, W., Zheng, G., Gao, X. (2018). Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_16

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