{"title":"用于培养基优化的生物感知机器学习","authors":"Takamasa Hashizume , Bei-Wen Ying","doi":"10.1016/j.nbt.2025.07.006","DOIUrl":null,"url":null,"abstract":"<div><div>Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological fluctuations and experimental errors. To address these issues, we developed a medium optimization platform that integrates simplified and effective experimental manipulation, error-aware data processing for model training, predictive model construction to enhance accuracy and avoid local optimization, and an efficient optimization framework of active learning. Using this approach, we fine-tuned a 57-component serum-free medium for CHO-K1 cells, in which a total of 364 media were experimentally tested. The reformulated medium achieved approximately 60 % higher cell concentration than commercial alternatives. The improved cell culture is definitive toward CHO-K1, underscoring the platform's precision in targeted cell culture optimization. Our approach offers a robust tool for optimizing complex systems in cell culture and broader experimental studies, as well as in biomedical engineering applications.</div></div>","PeriodicalId":19190,"journal":{"name":"New biotechnology","volume":"89 ","pages":"Pages 141-151"},"PeriodicalIF":4.9000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Biology-aware machine learning for culture medium optimization\",\"authors\":\"Takamasa Hashizume , Bei-Wen Ying\",\"doi\":\"10.1016/j.nbt.2025.07.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological fluctuations and experimental errors. To address these issues, we developed a medium optimization platform that integrates simplified and effective experimental manipulation, error-aware data processing for model training, predictive model construction to enhance accuracy and avoid local optimization, and an efficient optimization framework of active learning. Using this approach, we fine-tuned a 57-component serum-free medium for CHO-K1 cells, in which a total of 364 media were experimentally tested. The reformulated medium achieved approximately 60 % higher cell concentration than commercial alternatives. The improved cell culture is definitive toward CHO-K1, underscoring the platform's precision in targeted cell culture optimization. Our approach offers a robust tool for optimizing complex systems in cell culture and broader experimental studies, as well as in biomedical engineering applications.</div></div>\",\"PeriodicalId\":19190,\"journal\":{\"name\":\"New biotechnology\",\"volume\":\"89 \",\"pages\":\"Pages 141-151\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1871678425000731\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"New biotechnology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1871678425000731","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Biology-aware machine learning for culture medium optimization
Cell culture technologies are widely used in academia and industry, yet optimizing culture media remains an art due to the complexity of cell-medium interactions. Machine learning has emerged as a promising solution, but it is hindered by biological fluctuations and experimental errors. To address these issues, we developed a medium optimization platform that integrates simplified and effective experimental manipulation, error-aware data processing for model training, predictive model construction to enhance accuracy and avoid local optimization, and an efficient optimization framework of active learning. Using this approach, we fine-tuned a 57-component serum-free medium for CHO-K1 cells, in which a total of 364 media were experimentally tested. The reformulated medium achieved approximately 60 % higher cell concentration than commercial alternatives. The improved cell culture is definitive toward CHO-K1, underscoring the platform's precision in targeted cell culture optimization. Our approach offers a robust tool for optimizing complex systems in cell culture and broader experimental studies, as well as in biomedical engineering applications.
期刊介绍:
New Biotechnology is the official journal of the European Federation of Biotechnology (EFB) and is published bimonthly. It covers both the science of biotechnology and its surrounding political, business and financial milieu. The journal publishes peer-reviewed basic research papers, authoritative reviews, feature articles and opinions in all areas of biotechnology. It reflects the full diversity of current biotechnology science, particularly those advances in research and practice that open opportunities for exploitation of knowledge, commercially or otherwise, together with news, discussion and comment on broader issues of general interest and concern. The outlook is fully international.
The scope of the journal includes the research, industrial and commercial aspects of biotechnology, in areas such as: Healthcare and Pharmaceuticals; Food and Agriculture; Biofuels; Genetic Engineering and Molecular Biology; Genomics and Synthetic Biology; Nanotechnology; Environment and Biodiversity; Biocatalysis; Bioremediation; Process engineering.