Modularization of biochemical networks based on classification of Petri net t-invariants.
Modularization of biochemical networks based on classification of Petri net t-invariants.
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DOI：
10.1186/1471-2105-9-90
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发表时间：
2008-02-08
影响因子：
3
通讯作者：
Koch I
中科院分区：
文献类型：
--
作者：
Grafahrend-Belau E;Schreiber F;Heiner M;Sackmann A;Junker BH;Grunwald S;Speer A;Winder K;Koch I
生物化学网络的结构分析是生物信息学和系统生物学中一个不断发展的领域。越来越多的生物数据从分子生物学网络的可用性承诺更深入的了解，但面临的问题，研究人员的组合爆炸。定性网络数据量的增长速度远远快于定量数据量，如酶动力学。在许多情况下，由于实验方法的限制或出于伦理原因，甚至不可能测量定量数据。因此，大量的定性数据，如交互数据，是可用的，但它没有充分用于建模目的，直到现在。新的方法已经开发出来，但数据的复杂性往往限制了许多方法的应用。生物化学Petri网使研究静态和动态定性系统特性成为可能。一种Petri网方法是基于系统不变属性的计算的模型验证，重点是t-不变量。T-不变量对应于描述系统基本行为的子网络。随着系统复杂性的增加，基本行为只能由大量的t-不变量来表示。根据我们的生物化学Petri网的验证标准，通过手动解释每个子网络（t-不变）来验证生物学意义是不可能的。因此，一个自动化的，有生物学意义的分类将有助于分析t-不变量，并支持所考虑的生物系统的基本行为的理解。在这里，我们介绍了一种新的方法来自动分类t-不变量，以科普网络的复杂性。我们应用聚类技术，如UPGMA，完全连锁，单连锁，和邻居加入结合不同的距离措施，得到生物学上有意义的集群（t-集群），它可以被解释为模块。为了找到要考虑用于解释的t聚类的最佳数量，应用聚类有效性度量Silhouette Width。我们认为两个不同的案例研究为例：一个小的信号转导途径（信息素反应途径在酿酒酵母）和一个中型的基因调控网络（基因调控杜氏肌营养不良症）。我们自动将t-不变量分类为功能不同的t-簇，这些t-簇可以在生物学上被解释为网络中的功能模块。我们发现不同的距离措施以及聚类方法的适用性。在生物学上有意义的分类的t-不变量，最好的结果是使用Tanimoto距离测量。考虑到聚类方法，所得到的结果表明，UPGMA和完全连锁是适合于聚类t-不变量的生物可解释性。提出了一种基于聚类分析的Petri网t-不变量生物分类方法。由于网络过程的生物学意义的数据简化和结构化，可以评估大量的t-不变量，从而允许定性生化Petri网的模型验证。这种方法也可以应用于基本模式分析。
Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied. We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability. We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.
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影响因子：
1.6
作者：
Heiner, M;Koch, I;Will, R
通讯作者：
Will, R
DOI：
10.1111/j.2044-8317.1976.tb00714.x
发表时间：
1976-01-01
影响因子：
2.6
作者：
HUBERT, L;SCHULTZ, J
通讯作者：
SCHULTZ, J
影响因子：
9.5
作者：
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通讯作者：
Chaouiya, Claudine
影响因子：
5.8
作者：
Ma, HW;Zhao, XM;Zeng, AP
通讯作者：
Zeng, AP
DOI：
10.1093/ietfec/e89-a.11.3166
发表时间：
2006-11-01
影响因子：
0.5
作者：
Matsuno, Hiroshi;Li, Chen;Miyano, Satoru
通讯作者：
Miyano, Satoru