Bioprocess optimization under uncertainty using ensemble modeling
Bioprocess optimization under uncertainty using ensemble modeling
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DOI：
10.1016/j.jbiotec.2017.01.013
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发表时间：
2017-02-20
影响因子：
4.1
通讯作者：
Gunawan, Rudiyanto
中科院分区：
文献类型：
--
作者：
Liu, Yang;Gunawan, Rudiyanto
基于模型的生物过程优化的性能取决于数学模型的精度。然而，由于缺乏模型的可辨识性，生物过程的模型往往具有很大的不确定性。在存在这样的不确定性的情况下，依赖于单个“最佳匹配”模型的预测的过程优化在现实生活中可能表现不佳，例如，由使用可用过程数据的最大似然参数估计产生的模型。在这项研究中，我们使用集成建模来解释生物过程优化中的模型不确定性。更具体地说，我们采用贝叶斯方法来定义模型参数的后验分布，在此基础上，我们使用参数置信域的均匀分布抽样来生成模型参数的集合。基于集成的过程优化包括使用均值-标准差效用函数最大化期望的生物过程目标(例如，产率或产品效价)的下置信限。在哺乳动物杂交瘤细胞培养的单抗批量生产中，我们展示了所提出的策略的性能和稳健性。(三)2017年提交人(S)。
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single "best fit" model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture. (C) 2017 The Author(s).