Metabolic model-based ecological modeling for probiotic design
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 taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show 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.
Data availability
Data from the study that we used to evaluate our method can be found at the following source:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA324129/Our method is implemented as the \emph{friendlyNets} package, available for download at \url{https://github.com/lanl/friendlyNets} along with re-formatted data and python scripts for the analysis found in this paper.
Article and author information
Author details
Funding
U.S. Department of Energy (255LANL2018)
- James D Brunner
Mayo Clinic
- Nicholas Chia
Los Alamos National Laboratory (Center For Nonlinear Studies)
- James D Brunner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2024, Brunner & Chia
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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