Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095-1592, USA.
Biotechnol J. 2012 Mar;7(3):343-53. doi: 10.1002/biot.201100186. Epub 2011 Oct 21.
Previous mathematical modeling efforts have made significant contributions to the development of systems biology for predicting biological behavior quantitatively. However, dynamic metabolic model construction remains challenging due to uncertainties in mechanistic structures and parameters. In addition, parameter estimation and model validation often require designated experiments conducted only for purpose of modeling. Such difficulties have hampered the progress of modeling in biology and biotechnology. To circumvent these problems, ensemble approaches have been used to account for uncertainties in model structure and parameters. Specifically, this review focuses on approaches that utilize readily available fermentation data for parameter screening and model validation. Time course data for metabolite measurements, if available, can further calibrate the model. The basis for this approach is explained in non-mathematical terms accessible to experimentalists. Information gained from such an approach has been shown to be useful in designing Escherichia coli strains for metabolic engineering and synthetic biology.
先前的数学建模工作为定量预测生物行为的系统生物学发展做出了重大贡献。然而，由于机械结构和参数的不确定性，动态代谢模型的构建仍然具有挑战性。此外，参数估计和模型验证通常需要仅为建模目的而进行的指定实验。这些困难阻碍了生物学和生物技术建模的进展。为了规避这些问题，已经使用集成方法来考虑模型结构和参数的不确定性。具体来说，本综述重点介绍了利用现成的发酵数据进行参数筛选和模型验证的方法。如果有可用的代谢物测量时间过程数据，则可以进一步校准模型。这种方法的基础以实验人员易于理解的非数学术语进行解释。已经证明，从这种方法中获得的信息对于设计用于代谢工程和合成生物学的大肠杆菌菌株是有用的。
