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
{"title":"Advancing microbial production through artificial intelligence-aided biology","authors":"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","doi":"10.1016/j.biotechadv.2024.108399","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":null,"pages":null},"PeriodicalIF":12.1000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biotechnology advances","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0734975024000934","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
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.
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.