North Dakota State University Fargo, North Dakota, United States
The microbial world harbors a myriad of members that seamlessly associate with each other. Concurrent associations among thousands of such members highlights microbiome complexity, which has important functional implications. Recent studies have shown that the overall abundance and diversity of microbes alone cannot explain the functioning of the whole community. Associations among microbial groups may determine the process outcome, highlighting the importance of gaining an insight beyond the level of relative abundance and diversity. Network theories can reveal complex associations within microbial communities and why some species occur together in niches. Network analysis can also be used to study and to conduct hypothesis-driven research on how network complexity can give early signals to microbiome response to environmental perturbations. Thus, there has been a surge in network analysis articles in recent years.
Statistical ‘associations’ in co-occurrence networks may not always represent true biotic ‘interactions’ and can even be confounded by habitat-filtering. Therefore, statistical associations may need to be verified through empirical validation. In this presentation, I will discuss the potential of synthetic biological approaches to identify true microbial interactions from networks.