Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks
MetadataShow full item record
Citation (published version)Demetrius DiMucci, Mark Kon, Daniel Segre. 2018. "Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks." MSYSTEMS, Volume 3, Issue 5. https://doi.org/10.1128/mSystems.00181-18
Microbes affect each other’s growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia.
RightsCopyright © 2018 DiMucci et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.