  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.
