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Identification of biochemical networks by S-tree based genetic programming

机译:通过基于S树的遗传规划识别生化网络

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Motivation: Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system.
机译:动机:大多数以前的生化网络建模方法要么集中于具有多个组件的网络结构的表征,要么集中于具有相对少量组件的网络的动力学参数的估计。但是,为了获得系统级的了解,我们应该检查组件之间的交互以及组件的动态行为。同时识别结构和参数的一个关键障碍是与要估计的相对大量参数相比,缺少数据。因此,对于给定的数据,有许多合理的网络,但是大多数都不可能存在于实际系统中。

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