Abstract
Condition-relevant biological networks occurring under environmental conditions such as disease, stress and stimulus describe functional interactions among genes and proteins. These networks provide a systems-level view of the mechanisms of biological processes in the cell. The principal challenge is that biological networks under specific condition remain unknown and must be inferred from gene expression (mRNA levels) in microarray data. Due to the increase availability of the protein interaction and genomic analysis from the Internet, they provide the opportunity to identifying the significant biological networks instead of only dependent on gene expression. However, current protein–protein interaction networks do not provide information about the condition(s) under which the interactions occur. Although numerous studies used microarray analysis and traditional statistical and clustering methods with well-known pathway databases to identify the individual genes during the disease processes, the important gene regulations remain unclear and hard to detect the new pathways they are involved in. Some recent signal transduction pathway detection methods used the graph theory approach to identify the signal pathways from noisy protein networks. They did not focus on the high-order dependency relationship among genes and did not take biological insights such as protein complexes into consideration. This dissertation aims to develop computational approaches for inferring, analyzing and validating biological networks of genes corresponding to expression data and recent protein networks. We first describe a computational framework to reconstruct the gene regulatory network from the microarray data using biological knowledge and constraint-based inferences. We apply d-separate criteria and conditional independency to filter the links in the gene regulatory networks. The workflow integrates the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences to predict the transcription factors that regulate the gene expressions. Second, we propose protein complexes prediction method based on the conserved networks from the orthologous proteins across species as the initial seed graphs and applied cumulative hyper-geometric testing to greedily add the protein into the seed graph. Finally, we combine gene regulation and protein complexes to develop a novel method for identifying significant different signal transduction pathways using Markov blanket and A* heuristic search methods. The former takes into consideration the high-order dependency relationships among genes and the latter extracts genes with significant different gene expressions. In the experiments, we tested the methods by applying them to the two networks: yeast and human prostate cancer. According to the evolutionary conserved network structure, we efficiently predict the correct yeast protein complexes. In yeast pheromone and cell wall integrity signal network, we not only identify the main chain of those well-known networks but also realize the order of the functional modules involved in the networks. We adopt the microarray datasets consists of 71 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. In biological regulation and signal networks, we identify 9 significant transcription factors between normal and cancer samples and the networks we extracted correctly map to the well-known prostate cancer-related pathways in KEGG database. The prediction denoted the androgen function, integrin signal, MAPK, WNT, immune, STAT/JAK and ubiquitin pathways may be involved in the development of the prostate cancer and the promotion of the cell death in cell cycle. Our approach is able to efficiently integrate microarray data and protein-protein interactions for the network identification and also understand the high-order dependency interactions and protein complexes. We are able to identify the genes and their networks related to prostate cancer that are validated by recent databases and published literature. Base on the prostate cancer-related genes databases, we got higher sensitivity than the previous methods. Those critical concepts of tumor progression from network-based analysis are useful to understand cancer biology and disease treatment.