Advancing microbial production through artificial intelligence-aided biology

IF 12.1 1区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Xinyu Gong , Jianli Zhang , Qi Gan , Yuxi Teng , Jixin Hou , Yanjun Lyu , Zhengliang Liu , Zihao Wu , Runpeng Dai , Yusong Zou , Xianqiao Wang , Dajiang Zhu , Hongtu Zhu , Tianming Liu , Yajun Yan
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引用次数: 0

Abstract

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.

通过人工智能辅助生物学推进微生物生产。
微生物细胞工厂(MCF)被用来构建可持续的高附加值化合物生产平台。为了优化新陈代谢并达到最佳生产率,合成生物学开发了各种基因装置,通过基因编辑、高通量蛋白质工程和动态调控来设计微生物系统。然而,目前的合成生物学方法仍然严重依赖人工设计、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其余挑战的关键。人工智能辅助微生物生产可在数秒内利用处理、学习和预测大量生物数据的能力,提供高概率的产出。通过训练有素的人工智能模型,传统的设计-构建-测试(DBT)循环已转变为多维的设计-构建-测试-学习-预测(DBTLP)工作流程,从而显著提高了操作效率,减少了劳动力消耗。在此,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,重点是基因组注释、人工智能辅助蛋白质工程、人工功能蛋白质设计和人工智能支持的通路预测。最后,我们讨论了将新型人工智能技术融入生物学的挑战,并提出了大型语言模型(LLM)在推动微生物生产方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biotechnology advances
Biotechnology advances 工程技术-生物工程与应用微生物
CiteScore
25.50
自引率
2.50%
发文量
167
审稿时长
37 days
期刊介绍: Biotechnology Advances is a comprehensive review journal that covers all aspects of the multidisciplinary field of biotechnology. The journal focuses on biotechnology principles and their applications in various industries, agriculture, medicine, environmental concerns, and regulatory issues. It publishes authoritative articles that highlight current developments and future trends in the field of biotechnology. The journal invites submissions of manuscripts that are relevant and appropriate. It targets a wide audience, including scientists, engineers, students, instructors, researchers, practitioners, managers, governments, and other stakeholders in the field. Additionally, special issues are published based on selected presentations from recent relevant conferences in collaboration with the organizations hosting those conferences.
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