Systems and data-driven approaches to understanding Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is an incurable, progressive and debilitating lung disease with a median survival of 3 years from time of diagnosis, worse than many cancers. There is a clear, currently unmet clinical need to understand the specific molecular mechanisms that drive this disease. The goal of this project is to apply integrated transcriptomic and proteomic techniques to systematically understand the molecular networks that drive the development of IPF. The biological focus will be on key cell signalling pathways, such as the Wnt pathway, that play a role in signalling between the epithelial and fibroblast cells in the lung. Using graph-based (network) approaches, this project will integrate omics data with prior knowledge and then extract mechanistic information to develop testable (wet-lab) hypotheses of fibrosis in lung disease. The project draws on cutting-edge techniques in data-science and computational systems biology and will provide the student with a thorough training in using these approaches as well as in validating identified genes/proteins and protein networks through wet-lab techniques.