Accelerating cell culture media development using Bayesian optimization-based iterative experimental design
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. F...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61113-5 |
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| author | Harini Narayanan Joshua A. Hinckley Rachel Barry Brendan Dang Lenna A. Wolffe Adel Atari Yuen-Yi Tseng J. Christopher Love |
| author_facet | Harini Narayanan Joshua A. Hinckley Rachel Barry Brendan Dang Lenna A. Wolffe Adel Atari Yuen-Yi Tseng J. Christopher Love |
| author_sort | Harini Narayanan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-178d0ca223404d8297cba2f6702f4bd0 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-178d0ca223404d8297cba2f6702f4bd02025-08-20T03:03:37ZengNature PortfolioNature Communications2041-17232025-07-0116111410.1038/s41467-025-61113-5Accelerating cell culture media development using Bayesian optimization-based iterative experimental designHarini Narayanan0Joshua A. Hinckley1Rachel Barry2Brendan Dang3Lenna A. Wolffe4Adel Atari5Yuen-Yi Tseng6J. Christopher Love7Koch Institute for Integrative Cancer Research at MITKoch Institute for Integrative Cancer Research at MITKoch Institute for Integrative Cancer Research at MITBroad Institute of MIT and HarvardBroad Institute of MIT and HarvardBroad Institute of MIT and HarvardBroad Institute of MIT and HarvardKoch Institute for Integrative Cancer Research at MITAbstract 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.https://doi.org/10.1038/s41467-025-61113-5 |
| spellingShingle | Harini Narayanan Joshua A. Hinckley Rachel Barry Brendan Dang Lenna A. Wolffe Adel Atari Yuen-Yi Tseng J. Christopher Love Accelerating cell culture media development using Bayesian optimization-based iterative experimental design Nature Communications |
| title | Accelerating cell culture media development using Bayesian optimization-based iterative experimental design |
| title_full | Accelerating cell culture media development using Bayesian optimization-based iterative experimental design |
| title_fullStr | Accelerating cell culture media development using Bayesian optimization-based iterative experimental design |
| title_full_unstemmed | Accelerating cell culture media development using Bayesian optimization-based iterative experimental design |
| title_short | Accelerating cell culture media development using Bayesian optimization-based iterative experimental design |
| title_sort | accelerating cell culture media development using bayesian optimization based iterative experimental design |
| url | https://doi.org/10.1038/s41467-025-61113-5 |
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