Application of Dynamic Path Analysis for Identification and Modification of Gene Regulatory Networks
Date Issued
2008
Date
2008
Author(s)
Hung, Kuo-Chih
Abstract
The study expands Path Analysis (PA), and adopts microarray time course data to identify gene regulatory networks (GRNs). It provides users degrees of confidence on GRNs in databases. In addition, defective networks can be modified based on modification indices in PA. A couple of approaches, such as Bayesian, Boolean, structural equation modeling and differential equations model, are used for the reconstruction of gene regulatory networks in databases. We generate several alternative networks as candidates from original networks in KEGG database for comparison. Furthermore, the static networks are expanded to dynamic form (multiple orders). Finally, path analysis may suggest the best one in the network pool based on various performance indices. In other words, this approach can evaluate the existing networks in gene networks databases and provide users degrees of confidence on each network. The gene regulatory networks are form KEGG in this study, including sub-networks of cell cycle –yeast, e.g., regulation of autophagy and MAPK signaling networks, and corresponding microarray time course data are adopted from NCBI database. Furthermore, we compare our approach with SSEM algorithm and dynamic Bayesian model method. Seven out of ten original GRNs in KEGG are ranked the best networks by our approach, and 43 percent of defective networks generated from seven best original GRNs can be correctly modified. Besides, we obtain better results than SSEM algorithm and the dynamic Bayesian model method do to the same networks. The true positive rate on the directed links of networks is at least 60 percent.
Subjects
Path Analysis
gene regulatory networks
performance index
modification index
Type
thesis
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