標題: Inferring S-system Models of Genetic Networks from a Time-series Real Data Set of Gene Expression Profiles
作者: Huang, Hui-Ling
Chen, Kuan-Wei
Ho, Shinn-Jang
Ho, Shinn-Ying
生物科技學系
Department of Biological Science and Technology
公開日期: 2008
摘要: It is desirable to infer cellular dynamic regulation networks from gene expression profiles to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. To cope with the problem "multiplicity of solutions", a sufficient amount of data sets of time-series gene expression profiles were often used. An efficient newly-developed method iTEA was proposed to effectively obtain S-system models from a large number (e.g., 15) of simulated data sets with/without noise. In this study, we propose an extended optimization method (named iTEAP) based on iTEA to infer the S-system models of genetic networks from a time-series real data set of gene expression profiles (using SOS DNA microarray data in E. coli as an example). The algorithm iTEAP generated additionally multiple data sets of gene expression profiles by perturbing the given data set. The results reveal that 1) iTEAP can obtain S-system models with high-quality profiles to best fit the observed profiles; 2) the performance of using multiple data sets is better than that of using a single data set in terms of solution quality, and 3) the effectiveness of iTEAP using a single data set is close to that of iTEA using two real data sets.
URI: http://hdl.handle.net/11536/2508
http://dx.doi.org/10.1109/CEC.2008.4631172
ISBN: 978-1-4244-1822-0
DOI: 10.1109/CEC.2008.4631172
期刊: 2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8
起始頁: 2788
結束頁: 2793
Appears in Collections:Conferences Paper


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