A hallmark of the joint disease osteoarthritis (OA) is the degradation of the articularcartilage in the affected joint, debilitating pain and decreased mobility. At present thereare no disease modifying drugs for treatment of osteoarthritis. This represents asignificant, unmet medical need as there is a large and increasing prevalence of OA.Using a systems biology approach, we aimed to better understand the pathogenicmechanisms of OA and ultimately aid development of therapeutics.This thesis focuses on the analysis of gene expression data from human OA cartilageobtained at total knee replacement (TKR). This transcriptomics approach gives agenome-wide overview of changes, but can be challenging to interpret. Network-basedalgorithms provide a framework for the fusion of knowledge so allowing effectiveinterpretation. The PhenomeExpress algorithm was developed as part of this thesis toaid the interpretation of gene expression data. PhenomeExpress uses known diseasegene associations to identify relevant dysregulated pathways in the data.PhenomeExpress was further developed into an 'app' for Cytoscape, the widely usednetwork analysis and visualisation platform.To investigate the processes that occur during the degradation of cartilage we examinedthe gene expression of damaged and intact OA cartilage using RNA-Seq and identifiedkey altered pathways with PhenomeExpress. A regulatory network driven by fourtranscription factors accounts for a significant proportion of the observed differentialexpression of damage-associated genes in the PhenomeExpress identified pathways. Wefurther explored the role of the cytokines IL-1 and TNF that have been reported to βdrive the progression of OA. Comparison of the expression response of in vitrocytokine-treated explants with the in vivo damage response revealed major differences,providing little evidence for any significant role of IL-1 and TNF as drivers of OA βdamage in vivo.Finally, we examined the heterogeneity of OA through analysis of cartilage expressionprofiles at TKR. Through a network-based clustering method, we found two subgroupsof patients on the basis of their gene expression profiles. These subgroups were found tohave distinct OA expression perturbations and we identified TGF and S100A8/9 βsignalling as potentially explaining the observed differential expression. We developeda RT-qPCR based classifier that allowed classification of new samples into thesesubgroups so allowing future assessment of the clinical significance of these subgroups.The work presented in this thesis includes a novel, widely-accessible tool for theanalysis of disease gene expression data, which we used to give new insights into thepathogenesis of osteoarthritis. We have produced a rich dataset for future research andour analysis of this data has increased our understanding of cartilage damage processesand the heterogeneity of OA.