Optimization of Chemical Defined Medium for Lactobacillus brevis Based on Artificial Intelligence

Area of Science:

• Microbiology
• Biotechnology
• Bioprocess Engineering

Background:

• Lactobacillus brevis (L. brevis) is recognized for lactic acid production and probiotic potential.
• Industrial fermentation of L. brevis faces challenges due to limited research and low productivity.
• Developing cost-effective media is crucial for large-scale L. brevis production.

Purpose of the Study:

• To create an economical synthetic medium for industrial-scale Lactobacillus brevis production.
• To identify specific nutrient requirements (amino acids, vitamins, nucleotides) for L. brevis growth.
• To optimize synthetic medium composition using AI and high-throughput screening.

Main Methods:

• Nutrient requirements of L. brevis were determined using the single nutrient omission method.
• Artificial intelligence algorithms and Growth Profiler 960 (GP960) were utilized for medium optimization.
• Comparative analysis of bacterial growth and cost was performed between synthetic and complex media.

Main Results:

• L. brevis requires 18 essential amino acids (excluding aspartic acid and glutamine), specific vitamins, and nucleotides.
• The optimized synthetic medium achieved a specific growth rate of 0.297 h⁻¹.
• The synthetic medium demonstrated equivalent L. brevis growth to complex media at a 57.52% cost reduction.

Conclusions:

• A cost-effective synthetic medium was successfully developed for L. brevis fermentation.
• The findings provide a foundation for advancing industrial-scale production of L. brevis.
• Optimized synthetic media offer a viable and economical alternative to complex media.