Cosenza Zachary, Block David E, Baar Keith, Chen Xingyu
Department of Chemical Engineering University of California Davis USA.
Department of Viticulture and Enology University of California Davis USA.
Eng Life Sci. 2023 Jun 28;23(8):e2300005. doi: 10.1002/elsc.202300005. eCollection 2023 Aug.
In this work, we applied a multi-information source modeling technique to solve a multi-objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum-free C2C12 cells using a hyper-volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade-off relationship between long-term growth and cost. We were able to identify several media with more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.5% of the cost of the control. These algorithmically generated media also maintained growth far past the study period, indicating the modeling approach approximates the cell growth well from an extremely limited data set.
在这项工作中，我们应用了一种多信息源建模技术，以使用超体积改进采集函数来解决一个多目标贝叶斯优化问题，该问题涉及同时将无血清C2C12细胞的成本最小化和生长最大化。在使用我们的贝叶斯标准设计的连续批次的定制培养基实验中，通过针对不同细胞生长动态的多种测定收集数据，该算法学会了识别长期生长和成本之间的权衡关系。我们能够识别出几种比对照具有更多C2C12细胞生长的培养基，以及一种成本仅为对照的62.5%但生长却多23%的培养基。这些通过算法生成的培养基在研究期之后仍保持生长，表明该建模方法从极其有限的数据集中能很好地近似细胞生长情况。
