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通过人工智能辅助生物学推进微生物生产.

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
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概括
此摘要是机器生成的。

人工智能 (AI) 通过加速设计-构建-测试-学习-预测循环来改变微生物细胞工厂 (MCF) 的生产. 这种由人工智能驱动的方法提高了效率,并减少了合成生物学中的手工劳动,以实现可持续的化合物制造.

关键词:
人工智能 (AI) 是一种人工智能.人工蛋白质设计的设计酶功能的预测预测基因组注释 基因组注释大型语言模型 (LLM)微生物生产的微生物生产.路径预测路径预测合成生物学 合成生物学

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科学领域:

  • 合成生物学和人工智能 (AI).

背景情况:

  • 微生物细胞工厂 (MCF) 对于可持续生产增值化合物至关重要.
  • 目前的合成生物学方法依赖于人工,劳动密集型的过程.
  • 人工智能提供强大的数据处理和预测能力来克服这些局限性.

研究的目的:

  • 审查人工智能辅助微生物生产方面的进展.
  • 突出AI在优化代谢工程和生产率方面的作用.
  • 讨论将人工智能,包括大型语言模型 (LLM) 整合到生物系统中.

主要方法:

  • 在基因组注释中对人工智能应用的审查.
  • 分析人工智能辅助的蛋白质工程和人工功能蛋白质设计.
  • 检查人工智能支持的路径预测方法.

主要成果:

  • 人工智能将传统的设计-构建-测试 (DBT) 循环转化为高效的设计-构建-测试-学习-预测 (DBTLP) 工作流.
  • 人工智能显著提高了运营效率,并减少了微生物生产中的劳动力.
  • 人工智能有助于从庞大的生物数据集中快速处理,学习和预测.

结论:

  • 人工智能对于解决微生物生产合成生物学方面的挑战至关重要.
  • 人工智能集成提高了效率,并减少了工程微生物系统的手工劳动.
  • 大型语言模型 (LLM) 显示了推动未来微生物生产战略的巨大潜力.