Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity

TL;DR

This paper introduces a Bayesian optimization method combined with autoencoders to efficiently generate synthetic microbiomes with minimized pathogenicity, advancing microbiome design for combating multidrug resistance.

Contribution

It presents a novel integration of Bayesian optimization and deep latent space encoding to identify microbiome variants with reduced MDR, improving search efficiency over traditional methods.

Findings

Bayesian optimization outperformed Thompson sampling in search efficiency.

Synthetic microbiomes with minimized MDR were successfully generated.

The approach enables targeted microbiome design with fewer biological tests.

Abstract

Synthetic microbiomes offer new possibilities for modulating microbiota, to address the barriers in multidtug resistance (MDR) research. We present a Bayesian optimization approach to enable efficient searching over the space of synthetic microbiome variants to identify candidates predictive of reduced MDR. Microbiome datasets were encoded into a low-dimensional latent space using autoencoders. Sampling from this space allowed generation of synthetic microbiome signatures. Bayesian optimization was then implemented to select variants for biological screening to maximize identification of designs with restricted MDR pathogens based on minimal samples. Four acquisition functions were evaluated: expected improvement, upper confidence bound, Thompson sampling, and probability of improvement. Based on each strategy, synthetic samples were prioritized according to their MDR detection. Expected improvement, upper confidence bound, and probability of improvement consistently produced synthetic microbiome candidates with significantly fewer searches than Thompson sampling. By combining deep latent space mapping and Bayesian learning for efficient guided screening, this study demonstrated the feasibility of creating bespoke synthetic microbiomes with customized MDR profiles.