  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 behavior of microbial
communities. We seek a modeling strategy that can capture emergent behavior
when built from sets of universal individual interactions. Our investigation
reveals that species-metabolite interaction modeling is better able to capture
emergent behavior in community composition dynamics than direct species-species
modeling.
  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 species-metabolite interaction models, and
conclude that only species-metabolite models have the necessary complexity to
to explain a wide variety of interdependent growth outcomes. We also show that
general species-species interaction models cannot match patterns observed in
community growth dynamics, whereas species-metabolite models can. We conclude
that species-metabolite modeling will be important in the development of
accurate, clinically useful models of microbial communities.
