Metabolic model-based ecological modeling for probiotic design

Journal Article · 20 February 2024 · eLife

DOI: https://doi.org/10.7554/eLife.83690 · OSTI ID:2310403

Brunner, James D. ; Chia, Nicholas

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.

• Sponsoring Organization: USDOE
• OSTI ID: 2310403
• Alternate ID(s): OSTI ID: 2310404; OSTI ID: 2315654; OSTI ID: 2475464
• Journal Information: eLife, Journal Name: eLife Vol. 13; ISSN 2050-084X
• Publisher: eLife Sciences Publications, Ltd.Copyright Statement
• Country of Publication: United States
• Language: English