A Foundation Model for Microbial Growth Dynamics
Abstract
Microbial growth dynamics contain key information about microbial populations that are critical in tasks such as identifying antibiotic resistance and estimating growth parameters. Previous studies show these dynamics can be encoded to lower dimensions. For specific applications, like microbial consortia engineering, generating high-quality datasets can be difficult due to experimental setups or massive parameters spaces. To address this, we developed a foundation model for microbial growth dynamics that enables researchers to extract meaningful features from limited datasets. Our model learns lower-dimension embeddings that distill key characteristics from raw growth data, and this enhances predictive performance in downstream applications such as quantifying antibiotic treatment and forecasting consortia behavior. By enabling researchers to derive actionable insights from limited datasets, our approach offers a powerful tool for advancing microbial research.
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Holmes, Zachary Austin (2025). A Foundation Model for Microbial Growth Dynamics. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/32815.
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