Optimization of muscle cell culturemedia using nonlinear design of experiments
Optimization of muscle cell culturemedia using nonlinear design of experiments
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DOI：
10.1002/biot.202100228
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发表时间：
2021-09-02
影响因子：
4.7
通讯作者：
Baar, Keith
中科院分区：
文献类型：
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作者：
Cosenza, Zachary;Block, David E.;Baar, Keith
由于需要广泛的实验、大量的培养基组分、非线性和交互的响应以及许多相互冲突的设计目标，为生物过程(如组织工程和养殖肉类生产中使用的那些)优化培养基是困难的。在这里，我们展示了一种非线性实验设计(DOE)方法的能力，与传统的DOE方法相比，该方法可以在更少的实验中预测最佳介质条件。该方法基于局部优化的坐标搜索与动态调整的搜索空间的混合，以及利用径向基函数来存储和建模先验知识的截断遗传算法的全局搜索方法。使用这种方法，我们能够降低肌肉细胞增殖液的成本，同时在播种后48小时保持细胞生长，使用典型商业培养液的30种常见成分比传统的DOE(70比103)少。虽然我们清楚地证明了实验优化算法显著优于传统的DOE，但由于选择了以介质成本加权的48h生长试验作为目标函数，这些发现仅限于单代的性能，并不能推广到多代的生长。这强调了选择与流程目标保持良好一致的目标函数的重要性。
Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number ofmedia components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design-of-experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48 h after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70 vs. 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48 h growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals.