Junyu Chen , Nilmani Singh , Jingxia Lu , Stephan T. Lane , Huimin Zhao
{"title":"Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering","authors":"Junyu Chen , Nilmani Singh , Jingxia Lu , Stephan T. Lane , Huimin Zhao","doi":"10.1016/j.copbio.2025.103380","DOIUrl":null,"url":null,"abstract":"<div><div>Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.</div></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":"96 ","pages":"Article 103380"},"PeriodicalIF":7.8000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in biotechnology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0958166925001247","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/11/4 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.
期刊介绍:
Current Opinion in Biotechnology (COBIOT) is renowned for publishing authoritative, comprehensive, and systematic reviews. By offering clear and readable syntheses of current advances in biotechnology, COBIOT assists specialists in staying updated on the latest developments in the field. Expert authors annotate the most noteworthy papers from the vast array of information available today, providing readers with valuable insights and saving them time.
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COBIOT is organized into themed sections, each reviewed once a year. These themes cover various areas of biotechnology, including analytical biotechnology, plant biotechnology, food biotechnology, energy biotechnology, environmental biotechnology, systems biology, nanobiotechnology, tissue, cell, and pathway engineering, chemical biotechnology, and pharmaceutical biotechnology.