Predicting microbial community structure and temporal dynamics by using graph neural network models.

ABSTRACT: Understanding species-level abundance dynamics in complex microbial communities is key to managing microbial ecosystems, yet it remains a major challenge. In wastewater treatment plants (WWTPs), the presence and abundance of process-critical bacteria are essential for removing or recycling pollutants. However, individual species can fluctuate without recurring patterns. Accurately forecasting these dynamics is critical for preventing failures and guiding process optimization. We have developed a graph neural network-based model that uses only historical relative abundance data to predict future dynamics. Each model is trained and tested on individual time-series from 24 full-scale Danish WWTPs (4709 samples collected over 3-8 years, 2-5 times per month). It accurately predicts species dyna

SUBMITTER: Andersen KS 

PROVIDER: S-EPMC12521489 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature