Metabolic Model-based Ecological Modeling for Probiotic Design

TL;DR

This paper develops a metabolic network-based model to predict and understand the success of probiotic bacteria engraftment in the human gut microbiome, linking microbial interactions to treatment outcomes.

Contribution

It introduces a generalized resource allocation metabolic modeling approach combined with dynamical systems to predict probiotic engraftment success and identify key microbial interactions.

Findings

Lotka-Volterra model accurately predicts engraftment outcomes

Network structure reveals potential drivers of probiotic success

Mechanistic insights into microbe-microbe interactions

Abstract

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.