The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities.

1The human gut microbiota comprises a dynamic ecological system that contributes significantly 2 to human health and disease. The ecological forces that govern community assembly and 3 stability in the gut microbiota remain unresolved. We developed a generalizable model-guided 4 framework to predict higher-order consortia from time-resolved measurements of lower-order 5 assemblages. This method was employed to decipher microbial interactions in a diverse 12-6 member human gut microbiome synthetic community. We show that microbial growth 7 parameters and pairwise interactions are the major drivers of multi-species community 8 dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial 9 interaction network as well as a top-down approach to community assembly pinpointed both 10 ecological driver and responsive species that were significantly modulated by microbial inter-