ArticleNPJ systems biology and applications2023
Employing active learning in the optimization of culture medium for mammalian cells.
Article in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 45 citations in OpenAlex.
- Model-Guided Optimization of a Low-Serum Medium for Madin-Darby Canine Kidney Cells Using a Kolmogorov-Arnold Network and Bayesian Optimization.Applied biochemistry and biotechnology · 2026Article
- From Primary Cells to Organoids: Expanding the Biological Relevance of In Vitro Cellular Models.Cells · 2026Review
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- Reverse bioengineering of the liver: developmental principles for next-generation liver-on-a-chip systems.Communications biology · 2026Review
- A data-driven evaluation of optimization techniques in cell culture media.Bioinformatics (Oxford, England) · 2026Article
- Towards Cost-Effective and Sustainable Media Formulations for Terrestrial and Aquatic Cellular Agriculture.Foods (Basel, Switzerland) · 2026Review
- MediaAssist: database to support the design and optimization of cell culture medium.Nucleic acids research · 2026Article
- Identifying key bioprocess variables using explainable machine learning to enhance culture efficiency and viability of umbilical cord-derived mesenchymal stem cells.International journal of medical sciences · 2026Article
- Bioprocess Engineering for Cultivated Meat.Advances in biochemical engineering/biotechnology · 2026Review
- Redefining media blending mathematically: a systematic approach for screening of medium components.Applied microbiology and biotechnology · 2025Article
- Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints.Scientific reports · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Accelerating cell culture media development using Bayesian optimization-based iterative experimental design.Nature communications · 2025Article
- Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.Communications biology · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Increasing the biomolecular relevance of cell culture practice.Journal of biomedical science · 2025Review
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Advancing Monoclonal Antibody Manufacturing: Process Optimization, Cost Reduction Strategies, and Emerging Technologies.Biologics : targets & therapy · 2025Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, respectively. Both modes successfully fine-tuned 29 components to generate a medium for improved cell culture. Intriguingly, the two modes provided different predictions for the concentrations of vitamins and amino acids, and a significant decrease was commonly predicted for fetal bovine serum (FBS) compared to the commercial medium. In addition, active learning-assisted medium optimization significantly increased the cellular concentration of NAD(P)H, an active chemical with a constant abundance in living cells. Our study demonstrated the efficiency and practicality of active learning for medium optimization and provided valuable information for employing machine learning technology in cell biology experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.