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Cell functions are highly complex as they involve a concerted activity of many genes and their products (proteins). Each cell activity is typically coordinated by the organization of the genome into sets of genes that co-regulate in concert and share common functions (regulatory modules), common roles (processes), or interactions together in pathways. Gene Regulatory Networks (GRN) organize these related cell activities coherently into a concise network representation. Regulatory interactions are inherently asynchronous and transpire at different times. GRNs with time delays are therefore represent the dynamic regulatory interactions reasonably well, which can be inferred from gene expressions (GE) time-series. Although genome-wide expression profiles provide important information about the cellular processes, due to the paucity of information available in the individual data sources, GRNs are better learned by using multiple types of data sources combined with GE data. In this thesis, we proposed several novel methods to infer and validate GRNs including delays from multiple data sources such as gene ontology databases, literature, GE time-series data, and quantitative protein-protein interaction networks (PPIN). Gene Ontology (GO) provides well-structured hierarchical vocabularies of biological processes and functional annotations of genes. It is commonly assumed that gene regulations/interactions occur among functionally associated genes. GO enrichment has been widely used to evaluate the functional coherence of a gene list, but has not been incorporated into evaluating similarity between genes. We incorporated GO enrichment to improve the functional similarities of genes and used them to extract functionally associated pathways. Most of previous research predicted gene interactions from GO functional similarities of genes by considering the hierarchical relations of GO terms, without/rarely considering their biological meaning, for example, the “regulate” relation. We proposed

Building and validating gene regulatory networks (GRN) with delays from multiple data sources