Microbial Interactions as Drivers of a Nitrification Process in a Chemostat

Area of Science:

• Microbial Ecology
• Bioprocess Engineering
• Mathematical Modeling

Background:

• Bioprocess modeling traditionally lacks detailed microbial community insights.
• Integrating microbial ecology data, such as operational taxonomic unit (OTU) abundances, presents challenges.
• Chemostat systems are valuable for studying microbial dynamics under controlled conditions.

Purpose of the Study:

• To incorporate microbial community data into bioprocess models.
• To analyze the utility and limitations of Lotka-Volterra models for nitrification processes.
• To develop and apply optimal tracking techniques for integrating genetic sequencing data into chemostat models.

Main Methods:

• Mathematical analysis of Lotka-Volterra models fitted to long-term chemostat data.
• Application of optimal tracking, a control theory technique, for data integration.
• Utilizing operational taxonomic unit abundances to reconstruct nitrogen forms in the reactor.

Main Results:

• The study discusses limitations and insights of using Lotka-Volterra models for nitrification.
• Optimal tracking successfully integrated genetic sequencing data into the chemostat model.
• The developed model reconstructs nitrogen forms using OTU abundances, offering insights into microbial growth rates.

Conclusions:

• Integrating microbial ecology data enhances bioprocess model understanding.
• Optimal tracking provides a framework for incorporating complex microbial data.
• The resulting explanatory model sheds light on microbial community behavior and nitrogen cycling in bioprocesses.