Building Predictive Understanding of the Activated Sludge Microbiome by Bridging Microbial Growth Kinetics and...

Area of Science:

• Microbial Ecology
• Bioinformatics
• Environmental Microbiology

Background:

• Current microbiome modeling approaches have limitations in predictive accuracy.
• Understanding microbial population dynamics is crucial for ecological insights.

Purpose of the Study:

• To develop a novel hybrid modeling approach integrating microbial growth kinetics and population dynamics.
• To enhance the predictive understanding of microbiomes in activated sludge systems.

Main Methods:

• Utilized 466 activated sludge samples and a data transformation technique.
• Applied Bayesian networks and topological data analysis to identify microbial guilds and keystone populations.
• Validated model inferences using the Microbial Database for Activated Sludge (MiDAS) and artificial neural networks.

Main Results:

• 36 out of 42 core populations exhibited stable dynamics (close to zero).
• The hybrid model incorporating microbial kinetic parameters improved prediction accuracy (Bray-Curtis similarity 0.70 vs 0.66).
• Identified keystone populations and time-dependent microbial interactions.

Conclusions:

• The proposed hybrid modeling framework offers a flexible and potentially adaptable approach for microbiome analysis.
• This method enhances predictive capabilities for time-dependent data in natural systems.
• The findings contribute to a deeper understanding of microbial ecology in engineered systems.