Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.

Area of Science:

• Biotechnology
• Metabolic Engineering
• Systems Biology

Background:

• Metabolic engineering optimizes microbial production of valuable substances by modifying genetics and cellular processes.
• Understanding metabolic pathways and regulation is crucial for successful engineering.
• Traditional methods struggle with the complexity of multi-omics data.

Purpose of the Study:

• To review the integration of systems biology and artificial intelligence (AI) in metabolic engineering.
• To highlight how this synergy can overcome current industrial biotechnology challenges.
• To focus on applications in producing food-related substances and compounds.

Main Methods:

• Utilizing multi-omics data (transcriptomics, proteomics, metabolomics).
• Applying computational methods and data mining.
• Leveraging Artificial Intelligence (AI) for data analysis and integration.

Main Results:

• Systems biology approaches enable dynamic, large-scale analyses.
• AI offers breakthroughs in data integration and holistic understanding.
• The combination of systems biology and AI enhances metabolic engineering efficiency.

Conclusions:

• The integration of systems biology and AI is vital for advancing metabolic engineering.
• This alliance provides solutions for complex data challenges in industrial biotechnology.
• Future applications hold significant promise for producing food-related compounds.