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A model reduction method for biochemical reaction networks

机译:生化反应网络的模型简化方法

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摘要

BackgroundIn this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model.
机译:背景技术在本文中,我们为生化反应网络提出了一种模型还原方法,该方法受多种可逆和不可逆酶动力学速率定律控制,包括可逆Michaelis-Menten和Hill动力学。该方法通过逐步减少络合物的数量来进行,络合物的数量定义为网络中反应的左侧和右侧。它基于加权拉普拉斯矩阵的Kron约简,它描述了网络中络合物和反应的图结构。正如我们所展示的,它不依赖于网络动态行为的先验知识,因此可以自动化。与原始网络相比,还原网络具有更少的复杂性,反应,变量和参数,但是还原网络中一组预先选择的重要代谢物的行为与原始网络类似。此外,简化的网络很大程度上保留了原始模型的结构和动力学。

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