Computational models can bring us closer to true interaction networks.

<p>A) Potential inhibitory mechanisms include direct inhibition of <i>C</i>. <i>difficile</i> by <i>Barnesiella</i> (e.g. via competition for scarce resources, or toxin production), or indirect inhibition (e.g. through a host antimicrobial response). B) A great deal has been published on the topic of network inference from complex data sets, and more can be done to improve inference methods. Particularly for microbial interaction networks, it is essential to identify, not only the nature of the interactions, but also the underlying mechanisms. Metagenomic genus abundance information can be used to infer causal relationships between bacteria; however, other information sources are required to determine the exact nature of these interactions. Each individual network edge may have very different underlying causes (metabolic, physical interaction, toxin-based, etc.). Including more tools in the pipeline, such as metabolic network reconstructions, bioinformatics tools, etc., will help elucidate these mechanisms, allowing far more rapid hypothesis generation, leading to a more focused effort in the wet lab.