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Updated: Jun 25, 2026
11:07
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
Published on: December 4, 2021
Modelling using rRNA-structured biomass models.
B Lavallée1, D Frigon, P Lessard
1Département de génie civil, Université Laval, Québec, G1K 7P4, QC, Canada. bernard.lavallee.1@ulaval.ca
Summary
New rRNA-structured biomass models explain cell growth and lag phases in activated sludge processes. These mechanistic models improve predictions for reactor design and operation.
Area of Science:
- Environmental microbiology
- Biochemical engineering
- Wastewater treatment
Background:
- Current activated sludge models use empirical functions for substrate flux distribution.
- Predicting biomass growth response and lag phases remains a challenge.
Purpose of the Study:
- To propose rRNA-structured biomass models for predicting cell metabolic status and growth response.
- To provide a mechanistic explanation for growth response and lag phases.
Main Methods:
- Development of rRNA-structured biomass models.
- Modeling the autocatalytic reaction rate of rRNA synthesis.
- Validation against experimental data from various reactor types.
Main Results:
- The proposed models accurately describe and predict biomass growth response.
- Mechanistic insights into growth response and lag phases were achieved.
- Model performance was validated across different reactor configurations.
Conclusions:
- rRNA-structured models offer a mechanistic basis for understanding activated sludge microbial dynamics.
- Intrinsic model parameters enhance applicability and improve reactor design.
- These models represent a significant advancement for activated sludge process optimization.
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