Metabolic blueprints of monocultures enable prediction and design of synthetic microbial consortia

Synthetic microbial ecology aims at understanding complex microbial dynamics and designing communities with desired properties, often based on mathematical models of individual organisms. However, it is unclear whether simplified models harbor enough detail to predict the composition of synthetic communities, especially in metabolically complex environments. Here, we use longitudinal exometabolite data of monocultures for 15 rhizosphere bacteria to parametrize a consumer-resource model, which we use to predict pairwise co-cultures and higher order communities. The capacity to artificially switch off cross-feeding interactions in the model demonstrates their importance in ecosystem structure. Leave-one-out and leave-two-out experiments reveal that pairwise co-cultures do not necessarily capture inter-species interactions within larger communities and broadly highlight the nonlinearity of interactions. Finally, we illustrate that our model can be used to identify new sub-communities of three strains with high likelihood of coexistence. Our results highlight experimental and theoretical challenges to be addressed in future efforts and establish hybrid mechanistic and data-driven metabolic models as a promising framework for predicting and engineering microbial communities.