Microbial Growth Foundational Model

Unmet Need

Microbial growth dynamics provide crucial insights into microbial populations, with applications in drug discovery, personalized medicine, and synthetic biology. Advances in the field have resulted in an ever-growing list of known microbe species and increasingly complex data. Understanding which microbes are important—and how each microbe influences the overall community—presents a computational challenge. Current modeling methods face limitations: experimental datasets are often sparse and costly to generate, models struggle with computational scalability, and traditional approaches require prior knowledge of microbial interactions. There is a need for a more efficient and broadly applicable tool to analyze microbial communities, extract meaningful insights, and predict microbial behavior across diverse conditions in these complex datasets.

Technology

Duke inventors have developed a foundation model for microbial growth dynamics using machine learning, intended to accurately model the interactions that occur in communities of multiple microbe species. This model extracts key features from limited datasets, enabling few-shot learning tasks such as antibiotic resistance identification and microbial consortia prediction. Unlike traditional models, this approach learns lower-dimensional representations of microbial growth, improving predictive performance while reducing computational complexity. The model can analyze vast microbial datasets without requiring prior knowledge of microbe interactions, making it a powerful tool for researchers working with complex or limited data. The model has been demonstrated to generate highly accurate growth curves with fast computational time, keeping pace with the growing size and complexity of microbial data.

Advantages

• Accurate modeling of microbial community dynamics using a foundation model trained on hundreds of thousands of growth curves
• Few-shot learning capability, allowing predictions from limited experimental data
• Versatile applications in antibiotic resistance analysis, synthetic biology, and microbiome engineering
• Faster computation with reduced data dimensions, making it scalable for large datasets
• No need for prior knowledge of microbial interactions