Biology subjects

Holmes, Z. A.

Publications and source records attributed to Holmes, Z. A..

3 recordsLinked to original sources

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

Linear scaling reveals low-dimensional structure in observable microbial dynamics

Microbial communities often exhibit apparently complex dynamics driven by myriad interactions among community members and with their environments. Yet, practical modeling and control are often based on limited number of observables, raising a fundamental question: to what extent are these observed dynamics predictable given unobserved background complexity? Here, we report an emergent simplicity that the temporal dynamics of observable microbial populations can be captured by low- dimensional representations. Using variational autoencoders (VAEs), we define a critical latent dimension (Ec) that quantifies the minimal number of variables required to represent observable microbial dynamics. We find that Ec scales linearly with the number of observables, despite the complexity of unobserved background dynamics. This principle holds across simulations of ecological, spatial, and gene-transfer models, experiments with engineered and environment-derived communities, and human microbiomes. Our findings establish a scaling law for microbial community dynamics and demonstrate observable dynamics alone contain sufficient information for prediction and control, even without full knowledge of the community.

systems biology↗

bioRxiv2024

A sequence- and structure-based characterization of microbial enzymes identifies P. stutzeri as a plastic-degrading species

Plastic waste has accumulated rapidly in the past century and is now found throughout every ecosystem on Earth. Its ubiquitous presence means that plastic is routinely ingested by countless organisms, with potential negative consequences for organismal health. New solutions are urgently needed to combat plastic pollution. Among the many strategies required to curb the plastic pollution crisis, the bioremediation of plastic via enzymatic activity of microbial species represents a promising approach. Diverse microbes harbor enzymes capable of degrading plastic polymers and utilizing the polymers as a carbon source. Herein, we characterize the landscape of microbial protein-coding sequences with potential plastic degrading capability. Using the two enzyme systems of PETase and MHETase as a guide, we combined sequence motif analysis, phylogenetic inference, and machine learning-guided 3D protein structure prediction to pinpoint potential plastic-degrading enzymes. Our analysis platform identified hundreds of enzymes from diverse microbial taxa with similarity to known PETases, and far fewer enzymes with similarity to known MHETases. Phylogenetic reconstruction revealed that the plastic degrading enzymes formed distinct clades from the sequences of ancestral enzymes. Among the potential candidate sequences, we pinpointed both a PETase-like and MHETase-like enzyme within the bacterium Pseudomonas stutzeri. Using plate clearing assays, we demonstrated that P. stutzeri is capable of degrading both polyurethane (Impranil(R)) and polycaprolactone (PCL). Pseudomonas stutzeri also grew on carbon-free agar supplemented with polystyrene, suggesting this organism can utilize synthetic polymers as a carbon source. Overall, our integrated bioinformatics and experimental approach provides a rapid and low-cost solution to identify and test novel polymer-degrading enzymes for use in the development of plastic bioremediation technologies.

microbiology↗