Study On Gene Regulatory Network Inference Based On Single-Cell Transcription And Gene Knockout

Gene Regulatory Network(GRN)is a key tool for processing and interpreting the vast amount of genetic information.It is used to describe complicated functional pathways in a given cell or tissue,which represent living processes such as metabolism,gene regulation,transport mechanisms or signal transduction.GRN plays an important role in the field of drug design,cancer monitoring and treatment,and its modeling and simulation are currently emerging bioinformatics research fields.The large and diverse gene expression information generated by the next-generation gene sequencing method provides a large amount of data basis for the inference of gene regulation network.However,due to the complexity of the distribution of genetic data and the random dynamic characteristics of gene transcription,the GRN inference is still a huge challenge.Traditional gene regulatory network inference methods infer the gene regulatory network through using the average value of gene expression in the cell to analyze the relationship between genes.These methods are difficult to identify the heterogeneity of gene expression in the cell.The improvement of gene sequencing technology provides a possibility to solve the problem.By analyzing the single-cell transcription data,the heterogeneity information of gene expression in the cell can be obtained.However,only using this data to infer GRN lacks the expression information of genes when they reach steady state,and the high time dimension of single-cell data leads to a high time complexity of the algorithm.In order to solve the problems in the traditional GRN inference method,this paper carried out the GRN inference method based on gene knockout data and single-cell transcription data.The research content is as follows:(1)In view of the problems of gene regulatory network inference methods lacking cell heterogeneity information,low inference accuracy of gene combination regulation,and high time complexity in the analysis of high-dimensional single-cell data,this paper constructs a GRN inference model based on gene classification and single-cell transcription.Firstly,a gene classification algorithm is designed.The gene expression profiling technology is used to collect steady-state gene expression data in multiple cells,and the data is analyzed to obtain gene categories;then the gene categories are used as a priori knowledge to analyze single-cell transcription data to obtain regulatory relationship for each gene.The experimental results show that the model can analyze the diversified information contained in the two types of data,establish a more accurate gene regulatory network,and at the same time improve the computational efficiency.The model provides a new solution to the problem of processing single-cell data with large data sets and high computational complexity.(2)In the inference results of the existing gene regulatory network inference methods,there are many false positive regulation and indirect regulation of misjudgment,which lead to low accuracy of GRN inference.Aiming at the two misjudgment results,this paper constructs a GRN inference model based on gene perturbation.The model designs corresponding gene knockout experiments to collect gene perturbation data and analyzes it,so as to judge the regulatory relationship of misjudgment in the current GRN inference results.It has been experimentally verified that the gene perturbation data collected by the model can observe the steady-state expression level of all other genes during the gene knockout or perturbation,and its analysis better captures the regulatory relationship between genes.The model further optimizes the inference results of the gene regulatory network,and provides a new theoretical and experimental method for establishing a more accurate gene regulatory network.Gene regulatory network is a hot issue in the field of genomics research.This study provides a new idea for establishing a more accurate gene regulatory network and revealing the network relationship between genes,proteins and small molecules in cells.