Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.
Keywords: Bayesian; correlation network; metagenomic; microbial ecology; networks; omics.
The study of the contribution of the microbiome to health and disease offers a promising avenue to better understand disease processes and offer more personalized therapies. These communities comprise thousands of species and strains that interact among each other and with the host in a variety of complex ways to influence patient health. Studying these communities as a network of interactions, rather than looking at specific species or functions individually, will provide a more comprehensive understanding of how the microbiome contributes to disease processes. Here, we describe the current methods for studying microbiome networks and interpreting how network features may be related to health outcomes.