  The microbial community composition in the human gut has a profound effect on
human health. This observation has lead to extensive use of microbiome
therapies, including over-the-counter ``probiotic" treatments intended to alter
the composition of the microbiome. Despite so much promise and commercial
interest, the factors that contribute to the success or failure of
microbiome-targeted treatments remain unclear. We investigate the biotic
interactions that lead to successful engraftment of a novel bacterial strain
introduced to the microbiome as in probiotic treatments. We use pairwise
genome-scale metabolic modeling with a generalized resource allocation
constraint to build a network of interactions between 818 species with well
developed models available in the AGORA database. We create induced sub-graphs
using the taxa present in samples from three experimental engraftment studies
and assess the likelihood of invader engraftment based on network structure. To
do so, we use a set of dynamical models designed to reflect connect network
topology to growth dynamics. We show that a generalized Lotka-Volterra model
has strong ability to predict if a particular invader or probiotic will
successfully engraft into an individual's microbiome. Furthermore, we show that
the mechanistic nature of the model is useful for revealing which
microbe-microbe interactions potentially drive engraftment.
