Efficient algorithms for ordinary differential equation model identification of biological systems
Efficient algorithms for ordinary differential equation model identification of biological systems
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DOI：
10.1049/iet-syb:20050098
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发表时间：
2007-03-01
影响因子：
2.3
通讯作者：
Wedelin, D.
中科院分区：
文献类型：
--
作者：
Gennemark, P.;Wedelin, D.
提出了从实验数据中识别常微分方程模型的结构和参数的参数估计和模型选择算法。这里提出的工作集中在一个未知的结构和一些时间过程中的信息可用于每个变量进行分析的情况下，这是利用，使算法尽可能有效。该算法的目的是处理现实规模的问题，其中的反应可以是非线性的参数和数据可以是稀疏和嘈杂的。为了提高计算效率，参数估计大多是一次估计一个方程，给出了一个快速和准确的参数估计算法相比，其他算法在文献中。模型选择是用一种有效的启发式搜索算法完成的，其中结构是逐步建立的。两个测试系统，以前被用来评估识别算法，代谢途径和遗传网络。通过使用合理数量的模拟数据成功地识别了这两个测试系统。此外，可以处理现实水平的测量噪声。与用于这些测试系统的其他方法相比，所提出的算法的主要优点是，一个完全指定的模型，而不仅仅是一个结构，被识别，并且与其他识别算法相比，它们相当快。
Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.