| BackgroundComputational neuroscience is a science using mathematical analysis and computer simulation at different levels to simulate and study the nervous system, one of whose main studies is to explore the process of brain development and its molecular mechanism in nervous system. Nervous system includes central nervous system and peripheral nervous system, and central nervous system includes brain and spinal cord. Development of the brain is affected by many factors, where the regulation of thousands of genes in neurons plays a key role. Studies have shown that these genes play important regulatory functions in the following three scales. (1) A large number of genes in accordance with specific spatial and temporal expression control the development of different brain regions in different fetal stages. (2) These genes are divided into some gene clusters, which are affected and regulated each other, and form very complex associated networks. (3) These genes and gene clusters are associated and affected in time and space, whose ultimate result is that the correct guidance of brain development. The gene regulations at three levels are corresponded to the complex associated process of genetic information in different scales. Therefore, we can study the process of brain development by multiscale analysis.We have got gene chip data in three parts of the developing human fetal brain (cerebellum, cerebral cortex and hippocampus) in the preliminary work of this study, each part of which contains information of 10,080 genes at seven point times. In this paper, we used multiscale analysis and appropriate mathematical models to predict the gene clusters and their regulatory networks in the three parts based on the information of these gene expression data.MethodsThe research methods of this thesis mostly focused on in the following areas.(1) Broadening the conditions for data preprocessing. According to the basic requirements of gene expression and the needs of this study, as long as the gene ratio> 0 at the seven time points, we believe that the gene may have a role in the gene regulatory process, which makes an preliminary gene selection and then we eliminate the genes which has nothing to do with the gene regulations by other models.(2) Establishment of gene chip model named y ~ n curve. There is no stable function between genes and their expressions in gene chip; therefore, we created a model y ~ n curve of gene chip, whose advantages were proved by using it to denoise gene chip data. We further analyzed the role of the model by using multiscale analysis and the best wavelet function of every gene chip.(3) Construction of multiscale cluster model to get gene clusters. One of traditional analysis methods of gene data is clustering. We made multiscale analysis on the basis of the y ~ n curve and clustered on each scale. Only when the genes on every scale were clustered into the same category, we incorporated them into a gene cluster.(4) Establishment of integer nonlinear programming model of regulatory networks based on objective weight to predict gene clusters. We took the average expression level of every cluster as its expression, and weighed every cluster with entropy, respectively taking the cerebellum, cerebral cortex and hippocampus as a whole. Then, we established the model of integer nonlinear programming based on weight matrix whose results were compared with that of the model of correlation coefficient. Only when their results were accordant, the regulatory relationships were admitted.ResultsWe used SPSS to solve cluster analysis, used LINGO to solve optimization model and used MATLAB R2007a to solve the other models.Then, we got the following main results.(1) There were gene expressions of 1153 genes in cerebellum, 956 genes in cerebral cortex and 1106 genes in hippocampus at 7 time points after data preprocessed.(2) When gene data were denoised by y ~ n curve with 16 wavelet functions, we got the best effect were respectively db7 in cerebellum, db4 in cerebral cortex and db4 in hippocampus, and all of the signal-to-noises of y ~ n curves in the three parts were higher than that of the original data.(3) In the gene cluster analysis, there are 34 clusters of 402 genes in cerebellum, 23 clusters of 304 genes in cerebral cortex, 25 clusters of 384 genes in hippocampus and 49 genes in the three parts. (4) There are 30 gene clusters to be concerned with regulatory network which has 39 relations of positive regulation and 5 relations of negative regulation in cerebellum, 15 gene clusters to be concerned with regulatory network which has 6 relations of positive regulation and 11 relations of negative regulation in cerebral cortex, and 16 gene clusters to be concerned with regulatory network which has 20 relations of positive regulation and 5 relations of negative regulation in hippocampus.(5) The five genes named IFITM3, H2AFY, SSRP1, SCAP and CD59 are participated in the gene clusters regulatory networks in cerebellum, cerebral cortex and hippocampus at the same time.Conclusion(1) The use of y ~ n curves can not only less lose of the information of gene chip data, but also has better denoising effect than the raw data, so it is an effective tool of gene chip data analysis.(2) As a new clustering method of gene chip, multiscale clustering will bring about a meaningful innovation, which can cluster more accurately, therefore it is a useful and novel statistical method, and can be extended to other areas.(3) The development and function of developing human fetal brain are regulated by genes, whose gene clusters and their regulations are very complex but valuable.(4) The nonlinear integer optimization model of this paper is more objective and reasonable in weight than matrix, which is used to study the regulation of gene clusters in the developing human fetal brain and good results have been got. The model can be used in the study of gene regulatory networks in other organizations and other species.(5) The five genes of IFITM3, H2AFY, SSRP1, SCAP and CD59 play a very important role in the gene regulatory process of the developing human fetal brain, which have been tested for association to diseases (hyperlipoproteinemia type II, diabetic angiopathies and prostatic neoplasms etc.). |
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