Glucocorticoids (GCs) have an important role in anti-inflammation, apoptosis and immunomodulatory activity. GCs exert their effect by binding to their receptor, glucocorticoid receptor (GR), which subsequently triggers receptor dimerisation, nuclear translocation and eventually causes impact on transcriptional activity. Such regulatory mechanism is complex as it is not only controlled at the transcription level, but also at the post translational level with other contributing factors such as protein stability and cofactor recruitment. Glucocorticoids are commonly used as part of the chemotherapeutical protocols for lymphoid malignancies and have been successfully implicated in treating childhood acute lymphoblastic leukaemia (ALL). Nevertheless, resistance and side effects such as muscle atrophy and osteoporosis still occur frequently.With the advance in high-throughput technology, vast amount of data on various scales, including genomics, proteomics, and metabolomics make the molecular study of cancer more complicated. The rise of systems biology helps the scientist to address this problem with the use of computation. Although the concept and the approach may vary depending on the research fields, the ultimate goal remains the same which is to create a comprehensive understanding of biological processes and to forecast outcome.The goal of this body of work is to better understand glucocorticoid induced apoptosis in acute lymphoblastic leukaemia by adopting a systems biology approach. As the Bcl-2 family, particularly Bim is known to be a key determinant of GC-induced apoptosis, we investigated the molecular mechanism of GC induction of Bim. By adopting ordinary differential equation modelling approach, we were able to make prediction and investigate details of Bim regulation by GCs. Further to this, we carried out an integrated microarray analysis in various ALL to study GC resistance and identified crucial candidate gene c-Jun as a regulator of Bim and Erg as a determinant for GC resistance. These results allowed us to refine our models and enabled more answers to be addressed. In conclusion, our findings not only suggest potential regulatory mechanisms for determining GC sensitivity, they also aid us to find potential biomarkers for determining GC resistance. More importantly, this study represents a successful example for utilising systems biology to study the genetic complexity in cancer.