About the project
Lung fibrosis kills thousands annually with no curative treatment. This project decodes the cellular and molecular mechanisms driving fibrosis initiation and progression, combining human models, spatial multi-omics, and AI/ML-based computational analysis of patient lung tissue. You will generate and interpret cutting-edge multimodal data to identify regulatory drivers and biomarkers.
Lung Fibrosis (IPF: Idiopathic Pulmonary Fibrosis, ILD: Interstitial Lung Diseases) are devastating chronic conditions characterised by lung scarring and irreversible respiratory decline.
This project investigates the cellular and molecular mechanisms that maintain tissue health and failure in fibrosis. Working across experimental and computational approaches, you would use human primary culture models alongside next-generation sequencing assays to generate datasets (single-cell, spatial). The datasets will be integrated with existing multimodal spatial atlases to study mechanisms underlying fibrosis.
Computationally, the project will develop and apply AI/ML-based integration frameworks and gene regulatory network inference tools to identify master transcriptional regulators and candidate biomarkers across fibrotic progression. These markers, testable hypotheses will be validated in experimental models.
The project carries strong multidisciplinary co-supervision spanning basic research, clinical science, and computational biology, with active collaborations across Southampton Faculties (SoBS, Medicine).
This environment is equally suited to:
- ambitious experimentalists seeking to develop quantitative computational skills
- computational candidates with strength, skills in machine learning or AI applied to biomedical and omics data
- candidates working at the wet-dry interface in lung biology (IPF/ILD research)
Training
Training will cover primary cell culture, NGS assay development, single-cell and spatial data analysis, and high-performance computing. This is an ambitious project suited to a highly motivated candidate with genuine enthusiasm for both experimental biology and quantitative analysis. The student will engage with the wider respiratory, genomics communities through cross-faculty seminars.
Additional training in relevant quantitative analysis (computational tools, statistics) and experimental biology would be provided. The Graduate School provides complementary training in scientific writing, grant awareness, project management, and career development. The student will attend cross-faculty seminars across SoBS, Medicine and engage with the wider respiratory, computational biology and machine learning communities.
