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Related Experiment Video

Updated: Jan 15, 2026

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

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

Kasper Skytte Andersen¹, Kai Zhao², Alexander de Linde Agerskov²

¹Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.

Nature Communications

|October 14, 2025

Summary

This summary is machine-generated.

Predicting microbial species abundance is crucial for ecosystem management. A new graph neural network model accurately forecasts these dynamics in wastewater treatment plants (WWTPs) using historical data.

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Area of Science:

Microbiology
Bioinformatics
Machine Learning

Background:

Managing microbial ecosystems requires understanding species abundance dynamics.
Process-critical bacteria in wastewater treatment plants (WWTPs) are vital for pollutant removal.
Unpredictable fluctuations in microbial species abundance pose challenges for process stability and optimization.

Purpose of the Study:

To develop a predictive model for microbial species abundance dynamics.
To forecast future abundance patterns using only historical relative abundance data.
To assess the model's accuracy and applicability across different microbial datasets.

Main Methods:

Developed a graph neural network (GNN)-based model.
Trained and tested the model on longitudinal time-series data from 24 Danish WWTPs.
Validated the approach on a human gut microbiome dataset.

Main Results:

The GNN model accurately predicts species dynamics up to 10 time points (2-4 months) ahead.
Predictions extended to 20 time points (8 months) in some cases.
The "mc-prediction" workflow demonstrated suitability for diverse longitudinal microbial datasets.

Conclusions:

The developed GNN model offers a robust method for forecasting microbial abundance dynamics.
This predictive capability is essential for managing microbial ecosystems and optimizing processes like WWTPs.
The "mc-prediction" workflow shows broad applicability to various longitudinal microbial studies.