Numerical Researches On Gene Regulatory Network

Computational systems biology mainly deals with huge quantities of biological data by means of integrating and performing analysis with efficient algorithms and supercomputers. Along with the development of computer science and experimental technique of biology, computational systems biology has become one of the kernel scientific research areas in the 21st century. Comprehensive gene expression analysis and gene regulatory network (GNR) construction are addressed as grand challenges of computational systems biology nowadays. This dissertation contributes an application of computing science to genomics research with the aim to study GRN from the perspective of computational systems biology.Numerical methods which focus on GRN construction are proposed. On one hand, qualitative analysis on gene regulations from expression data is performed. On the other hand, simulation method of generating artificial GRNs is developed by considering specific network topology and dynamic stability. These methods are applied in GRN construction of mouse neural stem cell to predict important regulatory signal pathways and functions of key genes.The innovative results in the four aspects are described below:1. Stepwise Network Inference Method (SWNI) based on linear differential model is proposed to reconstruct sparse GRN from steady state data response to single gene perturbation. A regression subset-selection strategy is adopted to choose significant regulators for a given gene. Then it solves the small size problem for high-dimensional data in order to remove unreasonable limitation of maximum number of regulators by strict selection rules. The numerical experiments indcate that the SWNI is efficient, and outperforms the other methods with the increase in both network size and sparsity.2. Based on complex network theory, gene regulatory network simulation method (GN-Simulator) is developed. Study on the topology of real gene networks, the simulation method for generating artificial gene network is proposed according to the robust biological mechanism and dynamic stability. Numerical experiments demonstrate that large-scale artificial gene networks can be simulated with similar dynamics as real ones, and various synthetic gene expression data can also be generated by the GN-Simulator to provide efficient and reasonable estimation platform for algorithms performance assessment.3. The regulatory mechanism of mouse neural stem cell differentiation by dcf1 is explored on the genome-level. Based on the SWNI, numerical method combined with biological experiment is designed to construct GRN of mouse neural stem cell from public data integrated with laboratory data. Computing-based predictions that pou6f1 might significantly affect the differentiation of neural stem cells in mouse brain and play an important role in regulation of dcf1, are consistent with some findings of dcf1 and research of the POU-domain in the literature. Moreover, the predicted network lays a solid foundation for further exploration of dcf1 function by indicating that dcf1 locates in the downstream of the signaling pathway.4. Parallel computing is considered to construct and simulate the large-scale GRNs, thereby increase efficiency in network inference and meet the computing requirements for large-scale gene expression management preliminarily. With the parallel strategy, synthetical analysis is performed successfully on high through-put gene chip data of mouse neural stem cell and similar issues in computational systems biology will be solved effectively.