Global Sensitivity Analysis Challenges in Biological Systems Modeling
Global Sensitivity Analysis Challenges in Biological Systems Modeling
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DOI：
10.1021/ie900139x
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发表时间：
2009-08-05
影响因子：
4.2
通讯作者：
Pistikopoulos, E. N.
中科院分区：
文献类型：
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作者：
Kiparissides, A.;Kucherenko, S. S.;Pistikopoulos, E. N.
哺乳动物细胞培养系统，生产高价值的生物制品，如单克隆抗体，正越来越多地用于临床。将基于模型的实验设计（DOE）和基于模型的控制和优化与实际工业生物过程联系起来的完整框架可以帮助实验，从而降低成本。然而，高保真度模型具有包含大量参数的固有特性，这由于当前分析技术的限制而进一步复杂化，从而导致仅对少量参数进行实验验证。敏感性分析技术可以提供有价值的洞察模型的特点。传统上，敏感性分析在生物系统模型上的应用或多或少被视为黑箱操作。在目前的工作中，我们阐明的方面的灵敏度分析和识别，推理，最合适的一组灵敏度分析方法的应用高度非线性动态模型的背景下，生物系统。具体而言，我们进行计算实验抗体生产的哺乳动物细胞培养模型的不同复杂性和识别，以及地址，与这种“真实的生活”的模型相关的问题。总之，一种新的全球筛选方法（基于导数的全球灵敏度措施，DGSM）被证明是最具时效性和鲁棒性的替代既定的方差为基础的蒙特卡罗方法。
Mammalian cell culture system, produce high-value biologics, such as monoclonal antibodies, which are increasingly being used clinically. A complete framework that interlinks model-based design of experiments (DOE) and model-based control and optimization to the actual industrial bioprocess could assist experimentation, hence reducing costs. However, high fidelity models have the inherent characteristic of containing a large number of parameters, which is further complicated by limitations in the current analytical techniques, thus resulting in the experimental validation of merely a small number of parameters. Sensitivity analysis techniques can provide valuable insight into model characteristics. Traditionally, the application of sensitivity analysis on models of biological systems has been treated more or less as a black box operation. In the present work, we elucidate the aspects of sensitivity analysis and identify, with reasoning, the most suitable group of sensitivity analysis methods for application to highly nonlinear dynamic models in the context of biological systems. Specifically, we perform computational experiments on antibody-producing mammalian cell culture models of different complexities and identify, as well as address, problems associated with such "real life" models. In conclusion, a novel global screening method (derivative based global sensitivity measures, DGSM) is proven to be the most time-efficient and robust alternative to the established variance-based Monte Carlo methods.