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Robust Parameter Estimation During Logistic Modeling of Batch and Fed-batch Culture Kinetics

机译：分批和补料分批培养动力学逻辑模型期间的鲁棒参数估计

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Methods for robust logistic modeling of batch and fed-batch mammalian cell cultures are presented in this study. Linearized forms of the logistic growth,logistic decline,and generalized logistic equation were derived to obtain initial estimates of the parameters by linear least squares. These initial estimates facilitated subsequent determination of refined values by nonlinear optimization using three different algorithms. Data from BHK,CHO,and hy-bridoma cells in batch or fed-batch cultures at volumes ranging from 100 mL-300 L were tested with the above approach and solution convergence was obtained for all three nonlinear optimization approaches for all data sets. This result,despite the sensitivity of logistic equations to parameter variation because of their exponential nature,demonstrated that robust estimation of logistic parameters was possible by this combination of linearization followed by nonlinear optimization. The approach is relatively simple and can be implemented in a spreadsheet to robustly model mammalian cell culture batch or fed-batch data.
机译：在这项研究中提出了分批和补料分批哺乳动物细胞培养的鲁棒逻辑模型的方法。推导了线性增长，逻辑下降和广义逻辑方程的形式，以线性最小二乘法获得参数的初始估计。这些初始估计值有助于通过使用三种不同算法的非线性优化来后续确定精确值。使用上述方法测试了批量培养或补料分批培养的BHK，CHO和杂交瘤细胞在100 mL-300 L范围内的数据，并且针对所有数据集的所有三种非线性优化方法均获得了溶液收敛性。尽管逻辑方程由于具有指数性质而对参数变化敏感，但这一结果表明，通过线性化和非线性优化的组合，可以可靠地估计逻辑参数。该方法相对简单，可以在电子表格中实施，以对哺乳动物细胞培养批次或补料批次数据进行稳健建模。

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