期刊
TRENDS IN MICROBIOLOGY
卷 21, 期 7, 页码 325-333出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.tim.2013.04.009
关键词
Eco-Systems Biology; function prediction; integrated omics; machine learning; microbial communities; systematic measurements
资金
- ATTRACT programme grant [ATTRACT/A09/03]
- Aide a la Formation Recherche (AFR) [PRD-2011-1/SR]
- Luxembourg National Research Fund (FNR)
- Luxembourg Centre for Systems Biomedicine
- University of Luxembourg
Natural microbial communities are ubiquitous, complex, heterogeneous, and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and temporally resolved unique samples in line with a discovery-driven planning approach. Resulting datasets will allow the generation of solid hypotheses about causal relationships and, thereby, will facilitate the discovery of previously unknown traits of specific microbial community members. However, to achieve this, solid wet lab, bioinformatic and statistical methodologies are required to have the promises of the emerging field of Eco-Systems Biology come to fruition.
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