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Updated: Jun 22, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Advancing microbial production through artificial intelligence-aided biology.

Xinyu Gong1, Jianli Zhang1, Qi Gan1

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

Biotechnology Advances
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) transforms microbial cell factories (MCFs) production by accelerating the Design-Build-Test-Learn-Predict cycle. This AI-driven approach enhances efficiency and reduces manual labor in synthetic biology for sustainable compound manufacturing.

Keywords:
Artificial intelligence (AI)Artificial protein designEnzyme function predictionGenome annotationLarge language models (LLMs)Microbial productionPathway predictionSynthetic biology

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Area of Science:

  • Synthetic biology and artificial intelligence (AI).

Background:

  • Microbial cell factories (MCFs) are crucial for sustainable production of value-added compounds.
  • Current synthetic biology methods rely on manual, labor-intensive processes.
  • AI offers powerful data processing and predictive capabilities to overcome these limitations.

Purpose of the Study:

  • To review advancements in AI-aided microbial production.
  • To highlight AI's role in optimizing metabolic engineering and productivity.
  • To discuss the integration of AI, including large language models (LLMs), into biological systems.

Main Methods:

  • Review of AI applications in genome annotation.
  • Analysis of AI-aided protein engineering and artificial functional protein design.
  • Examination of AI-enabled pathway prediction methodologies.

Main Results:

  • AI transforms the conventional Design-Build-Test (DBT) cycle into an efficient Design-Build-Test-Learn-Predict (DBTLP) workflow.
  • AI significantly improves operational efficiency and reduces labor in microbial production.
  • AI facilitates rapid processing, learning, and prediction from vast biological datasets.

Conclusions:

  • AI is pivotal in addressing challenges in synthetic biology for microbial production.
  • AI integration enhances efficiency and reduces manual effort in engineering microbial systems.
  • Large language models (LLMs) show significant potential for advancing future microbial production strategies.