Dynamic modeling, kinetics estimation, and uncertainty analysis of batch fermentation for optimal production of a novel co‐polymeric dipeptide from a thermophilic bacterium
Year: 2025
Type: article
Abstract: BACKGROUND Biopolymers produced from a thermophilic bacterium induce better thermal stability and tensile strength than those produced from mesophilic bacterium. Developing dynamic models is crucial to providing an optimal design and scale‐up of a batch fermenter targeting biopolymer synthesis. This study aims to formulate certain dynamic models for the production of a novel copolymeric dipeptide (poly‐γ‐glutamic acid) while investigating the batch fermentation kinetics of Bacillus licheniformis. RESULTS Batch experiments were conducted at temperatures of 40 and 50 °C under varied glucose concentrations supplemented with nitrogen, which determined a maximum cell growth and higher biopolymer yield at optimal conditions of 50 °C and 80 g L−1 of glucose. The kinetics of dual substrate consumption, biomass growth, and biopolymer synthesis were estimated by fitting three dynamic models, namely, Monod multiplicative with Haldane inhibition, Bertolazzi, and Volterra to the experimental data. A global sensitivity analysis (GSA) was subsequently performed, which identified the impact of kinetic parametric uncertainties on the endpoint poly‐γ‐glutamic acid concentration. CONCLUSION The Volterra model revealed the best fits, as confirmed by a high coefficient of determination (R2 > 0.98) and minimum corrected‐Akaike's information criterion (AICc = −194.78) compared to the other models. The GSA results revealed that the maximum cell concentration, growth‐associated term of the product formation, and the initial nitrogen concentration should be optimized with higher precision to yield an optimal biopolymer titer. This work facilitates the implementation of model‐based optimization and control tasks, which could enhance the performance of an industrial‐scale batch fermenter operated with a thermophilic bacterium. © 2025 Society of Chemical Industry (SCI).
DOI: 10.1002/jctb.7892
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4410411627