The Co-regulated Gene Network Of Transcription Factors And MicroRNA For Complex Diseases

The occurrence and development of complex diseases is related to the complex regulation of multiple genes, which have been the focus and difficulty of bio-medical research. The growing development of mocdern biology and experimental technology bring opportunities for the further study of the mechanism of gene regulation. The study on gene regulatory network of complex diseases reveals its complex internal life phenomena and regulation that play veiry important roles in the diagnosis and treatment of complex diseases.This paper follows the ideas of system biology and molecular biology, combined with bioinformatics methods, and attempts to explain the the internal mechanism of complex disease in the co-regulated network of transcription factors and microRNAs (miRNAs) and describe the dynamic mechanism of co-regulated network. First of all, this paper introduces the methods,principles and related databases for the co-regulated network construction of complex diseases.Secondly, Two kinds of dynamic models describing the transcription process are presented, and a dynamic model for the co-regulated feed-forward loop of transcription factors and microRNAs is proposed. Then the methods,databases and the dynamics model of feed-forward loop are applied to the pancreatic cancer data from the TCGA database.The study uses fold change analysis and exact tests to obtain differential genes and differential microRNAs in pancreatic cancer from the normal and diseased samples. Then the co-expression relationship between these differential genes and microRNAs is constructed to predict the regulatory interactions using weighted correlation network analysis. The transcription factors of genes are predicted by matching the transcripts' upstream sequence and the position weight matrix. Based on TransmiR and ENCODE databases, the regulatory relationship of transcription factors to differential microRNAs are taken as a union. The obtained relationships are integrated to build the co-regulated network and 134 feed-forward loop motifs are acquired. Meanwhile, to model and analyze the transcription mechanism of feed-forward loops use diff-erential equations. Employing the Gaussian process prior depicts the latent expression profiles of transcription factors and the kinetic model is deduced by Bayesian framework. The optimization of the parameters of the kinetic model and the hyper-parameters kernel function were carried out by using single objective and multi-objective cultural genetic algorithm, which considers both gene expression and their derivatives. The simulation results show that our method can be used to predict the expression of the latent transcription factors and the model parameters well. Compared with the single objective optimization algorithm, the improved multi-objective optimization algorithm is more robust and makes the parameters estimation error of model decline, the estimation accuracy of the latent transcription factors improve.