Fed-batch optimization of alpha-amylase and protease-producing Bacillus subtilis using Markov chain methods

Area of Science:

• Biotechnology
• Biochemical Engineering
• Microbial Fermentation

Background:

• Bacillus subtilis is a key host for producing industrial enzymes like alpha-amylase.
• Fed-batch culture is crucial for maximizing recombinant protein production.
• Controlling fermentation parameters is essential for optimizing yield and minimizing byproducts.

Purpose of the Study:

• To develop and verify a stoichiometry-based model for Bacillus subtilis fed-batch culture.
• To optimize the feeding schedule for enhanced alpha-amylase production.
• To investigate the impact of decision variables on fermentation outcomes.

Main Methods:

• Development of a stoichiometry-based model with 14 state variables.
• Integration of Pontryagin's optimum principle with fermentation data for optimization.
• Application of Markov chain Monte Carlo (MCMC) and Metropolis-Hastings algorithms for parameter estimation.
• Experimental validation of the optimized feeding strategy.

Main Results:

• The developed model accurately predicted fermentation behavior.
• Optimization of the feeding schedule led to a predicted 14% increase in alpha-amylase productivity.
• The switching time from batch to fed-batch operation was identified as the most sensitive decision variable.
• Sporulation onset limited the full realization of predicted productivity gains.

Conclusions:

• A robust modeling and optimization framework was established for recombinant Bacillus subtilis fermentation.
• The study highlights the importance of precise control over feeding strategies for enzyme production.
• Further research could explore strategies to mitigate sporulation for even higher yields.