With modern high throughput technology revolving around the -omics, biological datasets are rapidly growing. System biology is therefore of value in extracting overarching and unbiased biological insights.
My project uses systems biology to gain holistic outlooks towards complex diseases like type 2 diabetes. It aims to address the gap in the understanding behind the molecular changes associated with insulin resistance via the Singapore Adult Metabolism Study (SAMs) dataset. Muscle tissue biopsies from patients are probed at the genomic, transcriptomic, proteomic and metabolomic levels. These multi-omics data would then be put into the context of pathways, physical interactions, and drug-disease association using a system biology approach. Framework used for this study can be applied to other complex diseases for a more holistic approach towards understanding the biology of diseases.
Centre for Computational Biology, 8 College Road, Singapore, 169857