biostudies-literatureHashizume TMEXT | Japan Society for the Promotion of Science (JSPS)20https://www.ebi.ac.uk/biostudies/studies/S-EPMC10229643biostudies-literatureUnknown9(1)Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectivNPJ systems biology and applicationsEmploying active learning in the optimization of culture medium for mammalian cells.PMC1022964319H0321521K19815Ozawa YYing BWHashizume TfalseEmploying active learning in the optimization of culture medium for mammalian cells.Medium optimization is a crucial step during cell culture for biopharmaceutics and regenerative medicine; however, this step remains challenging, as both media and cells are highly complex systems. Here, we addressed this issue by employing active learning. Specifically, we introduced machine learning to cell culture experiments to optimize culture medium. The cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used to find optimized media as pilot studies. To acquire the training data, cell culture was performed in a large variety of medium combinations. The cellular NAD(P)H abundance, represented as A450, was used to indicate the goodness of culture media. In active learning, regular and time-saving modes were developed using culture data at 168 h and 96 h, respectiv2023-01-01T00:00:00Z2023 May2025-04-21T21:57:56.076Z2025-04-21T21:57:56.076ZS-EPMC102296433725382510.1038/s41540-023-00284-7