The Reconstruct Of Gene Regulatory Networks Based On Bayesian Networks

Fifty years ago, Watson and Crick identified the physical structure of the DNA, thus starting a new age for biological research. Since then, Systems Biology has become an extremely important field in biology, which aims at deep insights into biological systems. The progress of system biology reveals the complexity of the interaction of mass genes, and thus traditional descriptive method in biology and analysis by disassenbing faces the cruel challenging. With the completion of Human Genome Project, the discovery of the gene function has become the new research interest in the Post-genome times. Using the microarray techniques, it is possible for scientists to discover the regulatory relationships between genes. The gene regulatory network, in definition, is the simulation or reconstruction of the mutual relations among expresed genes. Gene regulatory network helps us to understand in organisms which, where, when and how genes are expressed though observing visual model of gene expression. In this way, gene regulatory network has been widely applied in the research on relations between genes and diseases or drug target designs. Because the genetic regulatory network is a dynamics model which has nonlinear traits such as robustness, hierarchy and so on, the restruction of genetic regulatory network is to restruct the genetic interactional model though the massive gene expression data combined with some analysis and computational method to simulate system dynamic behaviors, which can take a sight of the inter-dependent relationships between genes. Contrarily, the model established can directhte futher biologic experiments. Based on the crossing of subjects on molecule biology, nonlinear maths and informatics science, the analysis and restruction of gene regulatory networks have been an important research field in post_genome era.This paper reviews for the mostly mathematic method and models to describe genetic regulatory systems that have been employed in Systems Biology and bioinformatics, such as clustering, directed graphs, Boolean networks model, linear combination model, weight matrices model, mutual-information networks model, differential equatins, Bayesian networks and so on. Every methods has their own advantage but also some limitation, there are no the best model for genetic regulatory system. Boolean networks model is mathematically trackable, and its simplicity allows examination of large systems. Howerer, it can not infer networks with feedback loops. Generally, continuous networks are used to analyze the genetic regulatory network and reconstruct it. Weight matrices model is also applied to research the genetic network earlier. It can solve the problems that whether there are interactinal actions between genes and describe the strength according to the weight, but this approach has some deficiency. For example, it can not describe the regulatory relation among different genes accurately. Linear model can also be used to find the network model from the microarray data easily, but it is not realistic that we suppose the genetic regulatory relationships are linear. Another disadvantage is that the linear model can only describe a single attractor. The information entropy method describes a probability relationship, but the regulatory relationships it describes are not correct enough. At present Bayesian network model is one of the widely used and utility model, because of it probability character and can deal with lose and hide variables, it has the excellence that other models are not have, and Bayesian networks are gradually become a hot research for gene regulatory network. At the same time, because of biological networks has modular and hierarchical characteristics, in this paper, we put the biological data to clustering analysis. Then based on the Bayesian network cluster analysis methods to reconstruct gene regulatory networks.The organizational structure of this paper is as follows:ChapterⅠ: Introduction. This chapter first simple introduced the background of this issue, the related biological knowledge of gene regulatory network, and then introduced the gene chip and the research status of gene chips. On the final, introduced gene regulatory networks status and the mayor problem of gene regulatory networks are now faced.ChapterⅡ: Gene regulatory networks. This chapter introduces the biological background of gene regulation expression, and the eukaryotic and prokaryotic expression regulation. Then introduced the gene chip data processing methods. Finally detailed account of the gene regulatory network model, and each model was analyaed, according to the advantages and disadvantages of each model indetified by the model used in this paperChaptⅢ: Learning Bayesian network based on immune evolutionary algorithm. This chapter focuses on using immune evolutionary algorithm building Bayesian networks, compared with the existing algorithm, and verify its accuracy. Then combined three-phase algorithm and immune evolutionary algorithm and proposed a two-stage algorithm to build Bayesian network, and use it on biological data, construct a yeast cell cycle gene regulatory networks.ChaptⅣ: Summary and Outlook. Summarizes the main research work of this paper, and expectation the future works.Many problems on genetic regulatory network are still not solves yet. Butwith the development of bioinformatics, more and more researchers will have aware of the importance of this field and turn to it. So with more understanding of bioinformatics, Human can get more genetic informations from the genetic regulatory networks.