Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

中文翻译：

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通过人工智能辅助生物学促进微生物生产


微生物细胞工厂（MCF）已被用来构建增值化合物生产的可持续平台。为了优化新陈代谢并达到最佳生产力，合成生物学开发了各种遗传装置，通过基因编辑、高通量蛋白质工程和动态调控来改造微生物系统。然而，当前的合成生物学方法仍然严重依赖手动设计、费力的测试和详尽的分析。新兴的人工智能（AI）和生物学跨学科领域已成为解决剩余挑战的关键。人工智能辅助微生物生产利用在几秒钟内处理、学习和预测大量生物数据的能力，提供高概率的输出。借助训练有素的人工智能模型，传统的设计-构建-测试（DBT）周期已转变为多维的设计-构建-测试-学习-预测（DBTLP）工作流程，从而显着提高运营效率并减少劳动力消耗。在这里，我们全面回顾了人工智能辅助微生物生产的主要组成部分和最新进展，重点关注基因组注释、人工智能辅助蛋白质工程、人工功能蛋白设计和人工智能路径预测。最后，我们讨论了将新颖的人工智能技术整合到生物学中的挑战，并提出了大型语言模型的潜力（LLMs ）促进微生物生产。