多目标贝叶斯算法自动发现低成本高增长的无血清介质用于细胞农业应用程序
Zachary Cosenza1, David E Block1,2, Keith Baar3
1Department of Chemical Engineering University of California Davis USA.
Engineering in life sciences
|August 3, 2023
概括
这项研究使用贝叶斯优化来寻找具有成本效益的细胞培养基,确定可显著提高C2C12细胞生长的选项,同时降低开支. 该方法有效地平衡了增长和成本,以优化细胞培养基的开发.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 细胞培养优化细胞培养优化
背景情况:
- 优化细胞培养基对于生物制药生产至关重要.
- 在媒体开发中,平衡成本和细胞生长是一个重大挑战.
研究的目的:
- 应用多信息源贝叶斯优化,同时降低成本和最大化细胞生长.
- 为了确定最佳的无血清介质配方,用于C2C12髓母细胞生长.
主要方法:
- 使用了多目标贝叶斯优化技术,并具有超体积改进获取函数.
- 采用定制介质的连续实验批量,以贝叶斯标准为指导.
- 结合多种测试以捕捉多样化的细胞生长动态.
主要成果:
- 与对照组相比,识别的介质配方产生显著更高的C2C12细胞生长.
- 发现了一个媒介,在控制成本的62.5%上,增长率增加了23%.
- 观察到超过研究期的持续细胞生长,验证了模型的预测准确性.
结论:
- 贝叶斯优化方法有效地导航媒体成本和细胞增长之间的权衡.
- 这种方法可以发现高性能,具有成本效益的细胞培养基.
- 建模方法准确地预测细胞生长,即使有有限的实验数据.
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