The Preliminary Research Based On Differential Equation Model Of Genetic Regulatory Network

Along with the development of Bioinformatics ,based on differential single-gene expression analysis and clusters of genes in terms of common functionality ,it has been an urgent need and hot issue to understand the underlying pattern and mechanism by analyzing the interaction between genes or proteins as a whole and constructing regulatory network.This thesis studies on gene regulatory network based on differential equation, including the selection of regulators and estimation of parameters. One of the difficulties in constructing gene regulatory network is that the amount of genes is much more than the numbers of measured times, so it is impossible to estimate effectively parameters. Because the regulators of per gene are limited according to biology, this paper selects the regulators of determined target genes at the beginning, and then constructs equations for target genes.Concerning to the selection of regulators, the following two aspects have been taken into account, one is that cluster can classify genes with related functions according to the similarities of their expression profiles; the other is that correlation coefficient may evaluate interaction between genes. Therefore we select several genes as regulators for each target gene using cluster and correlation coefficient in order to reduce dimensions.Concerning to the estimation of parameters, this thesis combines genetic algorithm with classical iterative methods, first , separately using L-M method and genetic algorithm to infer parameters, and then the estimation values of genetic algorithm are used as initial parameter values to supply for L-M method and perform L-M method again and observe whether the values is better or not.Concerning to the validity and superiority of the method, through the contrast experiment of artificial data set and yeast data set, it shows that the result of our method is better than traditional method, which reflects the effectiveness and superiority of our method in the research of gene regulation network.This thesis studies on the selection of regulators and solution of system, provides some useful thoughts and ideas. However, there are still many problems need to be studied further by cooperating with biologists.