Forecasting Urban Wastewater Microbiome Dynamics Using a Digital Twin Framework.

Area of Science:

• Environmental microbiology
• Computational biology
• Systems ecology

Background:

• Urban wastewater harbors complex microbial communities offering insights into public health.
• Current wastewater microbiome studies are largely descriptive, lacking predictive capabilities.
• Predictive modeling is crucial for understanding microbial dynamics and public health trends.

Purpose of the Study:

• To introduce a digital twin framework (Q-net) for forecasting microbial abundance in urban wastewater.
• To develop an interpretable generative model for wastewater microbiome analysis.
• To enable simulation of microbial trends under various scenarios.

Main Methods:

• Utilized a 30-week longitudinal metagenomic dataset from seven wastewater treatment plants.
• Developed and trained an interpretable generative model, Q-net, for microbial abundance forecasting.
• Employed conditional inference trees for model transparency and interpretability.

Main Results:

• Q-net achieved high-fidelity forecasting of temporal microbial dynamics (R² > 0.97 for key taxa).
• The model demonstrated exceptional accuracy at the final timepoint (R² = 0.998).
• Q-net successfully simulated realistic microbial trends under hypothetical scenarios.

Conclusions:

• Digital twins, like Q-net, can transform wastewater microbiome studies into dynamic, predictive systems.
• This approach has significant implications for environmental monitoring and microbial ecosystem modeling.
• The interpretable nature of Q-net enhances understanding of wastewater microbial ecology.