NDLI: Establishing large-scale gene regulatory networks using a gene-knowledge-embedded evolutionary computation method

Content Provider	IEEE Xplore Digital Library
Author	Li-Sun Shu Hui-Ling Huang Shinn-Jang Ho Shinn-Ying Ho
Copyright Year	2011
Description	Author affiliation: Department of Automation Engineering, National Formosa University, Yunlin 632, Taiwan (Shinn-Jang Ho) \|\| Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan (Hui-Ling Huang; Shinn-Ying Ho) \|\| Department of Information Management, Overseas Chinese University, Taichung 40721, Taiwan (Li-Sun Shu)
Abstract	With the increased availability of DNA microarray time-series data, establishing large-scale gene regulatory networks (GRNs) plays an important role in studying systems biology. S-system is well recognized as a suitable model to reconstruct the kinetic model of GRNs. However, the inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of nonlinear differential equations to be optimized. The existing method iTEA has shown to be effective in reconstructing GRNs. In this work, an improved version of iTEA is proposed (named iTEA2) for efficiently establishing large-scale GRNs by incorporating the domain knowledge of gene regulation into the proposed evolutionary computation method. High performance of iTEA2 arises mainly from the novel method of encoding chromosomes in using the intelligent genetic algorithm. Let I be the maximal number of genes that directly regulated each gene. iTEA2 uses a hybrid encoding method which consists of regulation strength, gene number regulated, and binary control parameters in a chromosome. The value of the strength parameter is the kinetic order and control parameters indicate that the up- and down-regulated kinetic orders are active or not. The total number of parameters encoded in a chromosome for one gene is 5I+2, which is independent upon N. The experimental results show that iTEA2 is significantly better than iTEA in terms of accuracy, convergence speed, and robustness in establishing large-scale GRN.
Starting Page	272
Ending Page	276
File Size	173756
Page Count	5
File Format	PDF
ISBN	9781424487271
e-ISBN	9781424487288
DOI	10.1109/CSAE.2011.5952849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Evolutionary computation Gene expression Microarray Biological cells Optimization Systems biology Genetic algorithm DNA Gene regulatory network Kinetic theory Bioinformatics S-system
Content Type	Text
Resource Type	Article

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