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<p>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 dynamics up to 10 time points ahead (2–4 months), sometimes up to 20 (8 months). The approach, implemented as the “mc-prediction” workflow, is also tested on other datasets, including a human gut microbiome, showing its suitability for any longitudinal microbial dataset.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Kasper Skytte Andersen,
  • Kai Zhao,
  • Alexander de Linde Agerskov,
  • Christian Bro Sørensen,
  • Trine Juhl Holmager,
  • Marta Nierychlo,
  • Miriam Peces,
  • Chenjuan Guo,
  • Per Halkjær Nielsen

摘要

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 dynamics up to 10 time points ahead (2–4 months), sometimes up to 20 (8 months). The approach, implemented as the “mc-prediction” workflow, is also tested on other datasets, including a human gut microbiome, showing its suitability for any longitudinal microbial dataset.