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Computing Difference Abstractions of Metabolic Networks Under Kinetic Constraints

机译:在动力学约束下计算代谢网络的差异抽象

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Algorithms based on abstract interpretation were proposed recently for predicting changes of reaction networks with partial kinetic information. Their prediction precision, however, depends heavily on which heuristics are applied in order to add linear consequences of the steady state equations of the metabolic network. In this paper we ask the question whether such heuristics can be avoided while obtaining the highest possible precision. This leads us to the first algorithm for computing the difference abstractions of a linear equation system exactly without any approximation. This algorithm relies on the usage of elementary flux modes in a nontrivial manner, first-order definitions of the abstractions, and finite domain constraint solving.
机译:最近,提出了一种基于抽象解释的算法,用于预测具有部分动力学信息的反应网络的变化。但是,它们的预测精度在很大程度上取决于要应用哪种启发法,以增加代谢网络稳态方程的线性结果。在本文中,我们提出一个问题,即在获得尽可能高的精度的同时是否可以避免这种启发式方法。这使我们得到了第一个用于精确计算线性方程组的差抽象的算法,而无需进行任何近似计算。该算法以非平凡的方式依赖于基本通量模式的使用,抽象的一阶定义以及有限域约束求解。

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