Modeling and optimization of microbial hyaluronic acid production by Streptococcus zooepidemicus using radial basis...

Area of Science:

• Biotechnology
• Bioprocess Engineering
• Biopolymer Production

Background:

• Hyaluronic acid (HA) is a crucial biopolymer with extensive biomedical and cosmetic applications.
• Increasing market demand necessitates enhanced HA production methods.
• Optimizing microbial fermentation processes is key to meeting this demand.

Purpose of the Study:

• To model and optimize amino acid supplementation for enhanced hyaluronic acid (HA) production in Streptococcus zooepidemicus.
• To develop and validate a novel RBF-QPSO approach for bioprocess optimization.
• To compare the efficacy of the RBF-QPSO method against traditional Response Surface Methodology (RSM).

Main Methods:

• Utilized a radial basis function (RBF) neural network for bioprocess modeling.
• Employed the quantum-behaved particle swarm optimization (QPSO) algorithm to optimize amino acid concentrations.
• Integrated RBF and QPSO (RBF-QPSO) to create a predictive model for HA yield.

Main Results:

• Predicted a maximum HA yield of 6.92 g/L with specific optimal concentrations of arginine, cysteine, and lysine.
• Experimental validation achieved an HA yield of 6.7 g/L, a significant increase from the control (5.0 g/L).
• The RBF-QPSO approach demonstrated slightly superior modeling and optimization performance compared to RSM.

Conclusions:

• The developed RBF-QPSO approach effectively models and optimizes amino acid addition for enhanced HA production.
• This method offers a robust tool for optimizing complex, multivariable bioprocesses.
• The findings contribute to more efficient and scalable hyaluronic acid manufacturing.