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利用动力学信息推进代谢模型。

Advancing metabolic models with kinetic information.

作者信息

Link Hannes, Christodoulou Dimitris, Sauer Uwe

机构信息

Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland.

Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland; Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland.

出版信息

Curr Opin Biotechnol. 2014 Oct;29:8-14. doi: 10.1016/j.copbio.2014.01.015. Epub 2014 Feb 14.

Abstract

Kinetic models are crucial to quantitatively understand and predict how functional behavior emerges from dynamic concentration changes of cellular components. The current challenge is on resolving uncertainties about parameter values of reaction kinetics. Additionally, there are also major structural uncertainties due to unknown molecular interactions and only putatively assigned regulatory functions. What if one or few key regulators of biochemical reactions are missing in a metabolic model? By reviewing current advances in building kinetic models of metabolism, we found that such models experience a paradigm shift away from fitting parameters towards identifying key regulatory interactions.

摘要

动力学模型对于定量理解和预测细胞成分的动态浓度变化如何产生功能行为至关重要。当前的挑战在于解决反应动力学参数值的不确定性。此外,由于未知的分子相互作用和仅被假定分配的调节功能,还存在重大的结构不确定性。如果代谢模型中缺少一个或几个生化反应的关键调节因子会怎样?通过回顾构建代谢动力学模型的当前进展,我们发现此类模型正经历从拟合参数到识别关键调节相互作用的范式转变。

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