Published May 11, 2022 | Version v1
Journal article Open

Multi-information source Bayesian optimization of culture media for cellular agriculture

  • 1. Department of Chemical Engineering,University of California Davis, Davis,California, USA
  • 2. Operations Research and InformationEngineering, Cornell University, Ithaca,New York, USA
  • 3. Departments of Neurobiology, Physiology,and Behavior and Physiology and MembraneBiology, University of California Davis, Davis,California, USA
  • 4. Department of Viticulture and Enology,University of California, Davis, USA

Description

Culture media used in industrial bioprocessing and the emerging field of cellular agriculture is difficult to optimize due to the lack of rigorous mathematical models of cell growth and culture conditions, as well as the complexity of the design space. Rapid growth assays are inaccurate yet convenient, while robust measures of cell number can be time-consuming to the point of limiting experimentation. In this study, we optimized a cell culture media with 14 components using a multi-information source Bayesian optimization algorithm that locates optimal media conditions based on an iterative refinement of an uncertainty-weighted desirability function. As a model system, we utilized murine C2C12 cells, using AlamarBlue, LIVE stain, and trypan blue exclusion cell counting assays to determine cell number. Using this experimental optimization algorithm, we were able to design media with 181% more cells than a common commercial variant with a similar economic cost, while doing so in 38% fewer experiments than an efficient design-of-experiments method. The optimal medium generalized well to long-term growth up to four passages of C2C12 cells, indicating the multi-information source assay improved measurement robustness relative to rapid growth assays alone.

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