ArticleNature communications2025
Accelerating cell culture media development using Bayesian optimization-based iterative experimental design.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- 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
- Reverse bioengineering of the liver: developmental principles for next-generation liver-on-a-chip systems.Communications biology · 2026Review
- Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.Comprehensive reviews in food science and food safety · 2026Review
- A data-driven evaluation of optimization techniques in cell culture media.Bioinformatics (Oxford, England) · 2026Article
- Biofabrication for spatial control of multiscale biological crosstalk in tissue models.npj biomedical innovations · 2026Review
- Evaluating Alternatives to Fetal Bovine Serum in the Development of Advanced Biomaterial-Based Tumor Models: Overcoming Challenges in Biofabrication.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Deletion of low-essentiality, secretion-associated genes enhances recombinant protein production in Komagataella phaffii.Microbial cell factories · 2026Article
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Pichia-CLM: A language model-based codon optimization pipeline forProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Systematic perturbation of cultures of K. phaffii by carbon co-feeding show gene signatures associated with production of recombinant proteins.Microbial cell factories · 2026Article
- Development of Cell Culture Media for Cultivated Meat Production.Advances in biochemical engineering/biotechnology · 2026Review
- Progressing Regenerative Medicine: Integrating Bioprinting Platforms for Stem Cell Applications.Stem cells international · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we apply a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show that this approach yields new compositions of media with cytokine supplementation to maintain the viability and distribution of human peripheral blood mononuclear cells in the culture. Second, we apply this framework to optimize the production of three recombinant proteins in cultivations of K.phaffii. We identified conditions with improved outcomes for both applications compared to the initial standard media using 3-30 times fewer experiments than that estimated for other methods such as the standard Design of Experiments. Subsequently, we also demonstrated the extensibility of our approach to efficiently account for additional design factors through transfer learning. These examples demonstrate how coupling data collection, modeling, and optimization in this iterative paradigm, while using an exploration-exploitation trade-off in each iteration, can reduce the time and resources for complex optimization tasks such as the one demonstrated here.
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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.