| Chapter title |
Metabolic Model Reconstruction and Analysis of an Artificial Microbial Ecosystem
|
|---|---|
| Chapter number | 10 |
| Book title |
Metabolic Network Reconstruction and Modeling
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7528-0_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7527-3, 978-1-4939-7528-0
|
| Authors |
Chao Ye, Nan Xu, Xiulai Chen, Liming Liu |
| 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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| Unknown | 5 | 50% |
Demographic breakdown
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Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 26% |
| Researcher | 3 | 16% |
| Lecturer | 2 | 11% |
| Student > Master | 2 | 11% |
| Student > Bachelor | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 3 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 32% |
| Environmental Science | 3 | 16% |
| Immunology and Microbiology | 2 | 11% |
| Biochemistry, Genetics and Molecular Biology | 2 | 11% |
| Unspecified | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 3 | 16% |