Abstract
Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species-metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species-species modelling. Using publicly available data, we examine the ability of species-species models and species-metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species-species interaction models and quadratic SMI models and conclude that only species-metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species-species interaction models cannot match the patterns observed in community growth dynamics, whereas species-metabolite models can. We conclude that species-metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.