Department of Chemical Engineering, University of California Davis, Davis, California, USA.
Operations Research and Information Engineering, Cornell University, Ithaca, New York, USA.
Biotechnol Bioeng. 2022 Sep;119(9):2447-2458. doi: 10.1002/bit.28132. Epub 2022 May 27.
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.
由于缺乏严格的细胞生长和培养条件的数学模型，以及设计空间的复杂性，工业生物加工和新兴的细胞农业领域使用的培养介质很难进行优化。快速生长测定虽然不准确但很方便，而细胞数量的可靠测量则很耗时，以至于限制了实验的进行。在这项研究中，我们使用多信息源贝叶斯优化算法对含有 14 种成分的细胞培养基进行了优化，该算法根据不确定性加权理想度函数的迭代细化来定位最佳的培养基条件。作为模型系统，我们使用鼠源 C2C12 细胞，利用 AlamarBlue、LIVE 染色和台盼蓝排斥细胞计数测定法来确定细胞数量。使用这种实验优化算法，我们能够设计出比具有类似经济成本的常见商业变体多 181%的细胞的培养基，而完成的实验数量比高效的实验设计方法少 38%。该最佳培养基在 C2C12 细胞的四个传代中均能很好地长期生长，表明多信息源测定法相对于单独使用快速生长测定法提高了测量的稳健性。
