[en] 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
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