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Numerical Matrices Method for Nonlinear System Identification and Description of Dynamics of Biochemical Reaction Networks

机译:非线性系统辨识的数值矩阵方法及生化反应网络动力学描述

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摘要

A flexible Numerical Matrices Method (NMM) for nonlinear system identification has been developed based on a description of the dynamics of the system in terms of kinetic complexes. A set of related methods are presented that include increasing amounts of prior information about the reaction network structure, resulting in increased accuracy of the reconstructed rate constants. The NMM is based on an analytical least squares solution for a set of linear equations to determine the rate parameters. In the absence of prior information, all possible unimolecular and bimolecular reactions among the species in the system are considered, and the elements of a general kinetic matrix are determined. Inclusion of prior information is facilitated by formulation of the kinetic matrix in terms of a stoichiometry matrix or a more general set of representation matrices. A method for determination of the stoichiometry matrix beginning only with time-dependent concentration data is presented. In addition, we demonstrate that singularities that arise from linear dependencies among the species can be avoided by inclusion of data collected from a number of different initial states. The NMM provides a flexible set of tools for analysis of complex kinetic data, in particular for analysis of chemical and biochemical reaction networks.
机译:基于动力学复合物对系统动力学的描述,已经开发了用于非线性系统识别的灵活数值矩阵方法(NMM)。提出了一组相关方法,包括增加有关反应网络结构的先验信息量,从而提高重构速率常数的准确性。 NMM基于一组线性方程式的解析最小二乘解以确定速率参数。在没有先验信息的情况下,将考虑系统中物种之间所有可能的单分子和双分子反应,并确定一般动力学矩阵的元素。通过根据化学计量矩阵或更一般的表示矩阵集来构造动力学矩阵,有助于包含先验信息。提出了一种仅从与时间有关的浓度数据开始确定化学计量矩阵的方法。此外,我们证明了通过包含从许多不同的初始状态收集的数据,可以避免物种之间线性相关性引起的奇异性。 NMM提供了一套灵活的工具来分析复杂的动力学数据,尤其是用于化学和生化反应网络的分析。

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