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Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem

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Book cover Metabolic Network Reconstruction and Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1716))

Abstract

Microbial communities are widespread in the environment, and to isolate and identify species or to determine relations among microorganisms, some ‘omics methods like metagenomics, proteomics, and metabolomics have been used. When combined with various ‘omics data, models known as artificial microbial ecosystems (AME) are powerful methods that can make functional predictions about microbial communities. Reconstruction of an AME model is the first step for model analysis. Many techniques have been applied to the construction of AME models, e.g., the compartmentalization approach, community objectives method, and dynamic analysis approach. Of these approaches, species compartmentalization is the most relevant to genetics. Besides, some algorithms have been developed for the analysis of AME models. In this chapter, we present a general protocol for the use of the species compartmentalization method to reconstruct a model of microbial communities. Then, the analysis of an AME is discussed.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China

    Chao Ye, Nan Xu, Xiulai Chen & Liming Liu

Authors
  1. Chao Ye
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  2. Nan Xu
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  3. Xiulai Chen
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  4. Liming Liu
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Corresponding author

Correspondence to Liming Liu .

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Editors and Affiliations

  1. Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy

    Marco Fondi

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Ye, C., Xu, N., Chen, X., Liu, L. (2018). Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem. In: Fondi, M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_10

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  • DOI: https://doi.org/10.1007/978-1-4939-7528-0_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7527-3

  • Online ISBN: 978-1-4939-7528-0

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