The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/286641 doi: bioRxiv preprint first posted online Mar. 21, 2018;
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Abstract
25Microbes affect each other's growth in multiple, often elusive ways. The ensuing 26 interdependencies form complex networks, believed to influence taxonomic composition, 27 as well as community-level functional properties and dynamics. Elucidation of these 28 networks is often pursued by measuring pairwise interaction in co-culture experiments. 29However, combinatorial complexity precludes the exhaustive experimental analysis of 30 pairwise interactions even for moderately sized microbial communities. Here, we use a 31 machine-learning random forest approach to address this challenge. In particular, we show 32 how partial knowledge of a microbial interaction network, combined with trait-level 33 representations of individual microbial species, can provide accurate inference of missing 34 edges in the network and putative mechanisms underlying interactions. We applied our 35 algorithm to two case studies: an experimentally mapped network of interactions between 36 auxotrophic E. coli strains, and a large in silico network of metabolic interdependencies 37 between 100 human gut-associated bacteria. For this last case, 5% of the network is 38 enough to predict the remaining 95% with 80% accuracy, and mechanistic hypotheses 39 produced by the algorithm accurately reflect known metabolic exchanges. Our approach, 40 broadly applicable to any microbial or other ecological network, can drive the discovery 41 of new interactions and new molecular mechanisms, both for therapeutic interventions 42 involving natural communities and for the rational design of synthetic consortia.