Construction Of Gene Regulatory Networks Based On Bayesian Approaches

With the development of gene chip technology, the way that massive gene expressiondata are combined with certain calculation methods can result in the construction of a generegulatory network. There are many models used in the construction of gene regulatorynetworks, Bayesian network model with its solid theoretical foundation, naturalrepresentation of the knowledge structure, flexible reasoning ability and convenientdecision-making mechanism makes it’s application range more widely, becoming apowerful tool for building gene regulatory networks.Using Bayesian methods to reconstruct gene regulatory networks has establishedmany research directions, such as information theory-based constraint method, priorknowledge integration, and large scale free network research and so on. Mutualinformation theory to construct the gene regulatory network can consider the impact of theother genes to this gene, but it only provides the function features of genes, can’t offercausal relationships between genes; prior knowledge integration can overcome the sparseproblem of a gene network but lack the experiments in time-series expression data, makingit impossible to obtain error sensitivity information on prior knowledge.This article summarized the research status of Bayesian approaches to construct thegene regulatory network, and made some improvements on this thesis, the followingspecific research work was completed:1. Combined the node ordering with path consistency algorithm based on conditionalmutual information, solving the problem that the network has no causal directions. Toachieve this purpose, we made some improvements on the graph splitting method: firstfiltered mutual information between a pair of nodes, then arranged substructures indescending order of Bayesian scores, and finally according to the arrangement chose the orientation of the edge between the same gene pair included in the different substructures;2. Used Gibbs distribution method to integrate with the one source and multi-sourcebiological prior knowledge respectively, and applied different confidence indicators toreduce the impact of inconsistencies between prior knowledge and data, and finally appliedthe MCMC algorithm and hill-climbing algorithm in the time-series expression data tobuild the gene regulatory networks to verify its effectiveness;3. The first method used10and50yeast genes in DREAM3respectively; the secondmethod selected the14genes (including3transcription factors) from the KEGG database,and a set of prior knowledge applied the data which Lee had proposed, and another set ofprior knowledge used the data Harbison had proposed; then a gene regulatory constructionexperiment system was built, which verified the effectiveness of this two methods.