Systems biology approaches for analysis of microarray data and their applications in biological discovery

In this thesis we take network biology approaches using large scale microarray expression data in order to gain insight in three biological problems.;First, using a compendium of cancer microarray expression profiles, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers.;Next, we use a T-cell expression compendia in order to identify the regulators of the T-regulatory (Treg) cells, key mediators of immune tolerance. Using a transcriptional regulatory network, we show that there are multiple regulators of this signature, in contrast to the well established notion that FOXP3 is the master regulator. We identify IKZF2 as another regulator of the Treg signature. By experimentally perturbing FOXP3 and IKZF2, we show that both transcription factors contribute to the regulation of genes in the Treg signature.;Finally, using matched microRNA-mRNA expression profiles from the NCI-60 collection of cancer cell lines, we probe the microRNA regulatory networks in cancer. We identify miRNA-224 as a potential regulator of gene-expresion changes involved in cell cycle regulation and demonstrate that alterations in miRNA-224 impact the phenotype of lung cancer cell lines in-vitro, suggesting a potential role for this microRNA in lung carcinogenesis.;These findings demonstrate that network based systems biology approaches applied to large scale expression profiles can be used advantageously to identify genetic mediators associated with disease.