COMMIT: Consideration of metabolite leakage and community composition improves microbial community models

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic models improves the quality of the draft models, measured by the genomic evidence for considered enzymatic reactions. We then devise an approach for gap filling, termed COMMIT, that considers exchangeable metabolites based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual models. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.