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Related Experiment Video

Updated: Jan 18, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Leveraging artificial intelligence for efficient microbial production.

Xinyu Gong1, Jianli Zhang1, Qi Gan1

  • 1School of Chemical, Materials and Biomedical Engineering, College of Engineering, the University of Georgia, College of Engineering, Athens, GA 30602, USA.

Bioresource Technology
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances microbial production by overcoming limitations in enzyme discovery, metabolic regulation, and bioprocess control. AI applications in protein engineering and bioprocess development are accelerating sustainable compound manufacturing.

Keywords:
Artificial intelligenceBioprocess controlMicrobial productionProtein mining and engineering

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Area of Science:

  • Biotechnology and Synthetic Biology
  • Bioprocess Engineering
  • Computational Biology

Background:

  • Microbial production offers a sustainable route to valuable compounds but faces challenges like limited enzymes and suboptimal processes.
  • Key bottlenecks include insufficient functional enzymes for pathway construction and inadequate tools for metabolic regulation and bioprocess optimization.
  • These limitations hinder the efficiency and scalability of industrial microbial manufacturing.

Purpose of the Study:

  • To review recent advancements in artificial intelligence (AI) applications for microbial production.
  • To highlight AI's role in addressing challenges in enzyme discovery, strain development, and bioprocess optimization.
  • To propose future directions for AI integration in microbial manufacturing.

Main Methods:

  • AI-driven protein mining for novel enzyme identification.
  • AI-assisted protein engineering for improved enzyme function and stability.
  • Machine learning models for predicting strain phenotypes and optimizing fermentation conditions.
  • Integration of AI tools across the upstream bioprocess development pipeline.

Main Results:

  • AI significantly improves the discovery and engineering of functional enzymes for microbial pathways.
  • AI provides predictive insights into strain phenotypes and fermentation parameters, enhancing productivity.
  • AI facilitates the development of more robust and efficient microbial cell factories.
  • AI streamlines upstream bioprocess development, accelerating time-to-market for microbial products.

Conclusions:

  • AI technologies are crucial for overcoming existing limitations in microbial production.
  • AI-driven approaches enhance enzyme capabilities, metabolic control, and bioprocess efficiency.
  • Future development and application of AI will further revolutionize sustainable microbial manufacturing.