title: A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes ofSea Urchin.
abstract: BACKGROUND
  Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology.
  
  
  RESULTS
  Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map's density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters ofSea UrchinmRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressedSea Urchingenes associated with temperature were determined by calculating the difference in the degree of each gene from different networks.
  
  
  CONCLUSIONS
  The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.
authors.affiliation: School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China.
authors.forename: Longlong
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China.
authors.forename: Tingting
authors.initials: T
authors.lastname: Zhao
authors.affiliation: School of Mathematical Sciences, Ocean University of China, Qingdao, 266100 People's Republic of China.
authors.forename: Meng
authors.initials: M
authors.lastname: Ma
authors.affiliation: Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101 People's Republic of China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
keywords: BP algorithm; Differentially expressed Sea Urchin genes; Gene regulatory network; Neural network model