Ensemble modeling and related mathematical modeling of metabolic networks
Ensemble modeling and related mathematical modeling of metabolic networks
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DOI：
10.1016/j.jtice.2009.05.003
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发表时间：
2009-11-01
影响因子：
5.7
通讯作者：
Liao, James C.
中科院分区：
文献类型：
--
作者：
Rizk, Matthew L.;Liao, James C.
代谢工程涉及操纵或修改代谢途径中的关键基因，以实现增加感兴趣物质生产的目标。虽然操纵的候选基因通常是通过直觉选择的，但许多有效的目标是非常重要的，因此可以作为数学建模的动机。有效的数学建模策略允许更深入地了解生物系统的行为，同时帮助确定候选基因的操纵。在本文中，我们总结了各种建模方法的优点和局限性，包括化学计量学和动力学方法，并使用一个简单的模型系统来说明它们的应用。随着代谢系统的数学建模变得更加广泛和易于实现，建模和实验之间的迭代有机会变得越来越共生，允许实验由计算驱动，计算由实验结果更新和完善。鉴于此，我们将重点分析新引入的集成建模方法。这种方法允许在计算和实验之间建立更紧密的关系，使这一目标更接近于通常的实践。(C) 2009台湾化学工程师学会。Elsevier B.V.版权所有。
Metabolic engineering involves the manipulation or modification of key genes in metabolic pathways to accomplish the goal of increasing the production of a substance of interest. While candidate genes for manipulation are commonly chosen through intuition, many effective targets are non-trivial and thus serve as the motivation for mathematical modeling. Effective mathematical modeling strategies allow for a greater insight into the behavior of the biological system, while helping to identify candidate genes for manipulation. in this article, we Summarize the advantages and limitations of various modeling methodologies, including both stoichiometric and kinetic approaches, using a simple model system to illustrate their application. As mathematical modeling of metabolic systems becomes more widespread and easy to implement, the iteration between modeling and experiment has the opportunity to become increasingly symbiotic, allowing for experiment to be driven by computation, and Computation to be updated and refined by the results of experiment. In this light, we focus Our analysis on the newly introduced ensemble modeling methodology. This approach allows for a tighter relationship between computation and experiment, bringing this goal a step closer to common practice. (C) 2009 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.