Data-Driven engineering of in-vitro disease models

The rise in chronic diseases and the quest for effective diagnostic tests/treatments is indicated as the greatest challenge to global health. On top of disproportionate healthcare and societal impacts, the economic burden imposed by chronic diseases is projected to reach $47 trillion by 2030. Counteracting the status quo will require the development of multidisciplinary approaches to understanding —and ultimately enabling personalised control of— pathological states. Specifically, the development of novel in vitro models offers a path to overcome road-blocks to treatments identification: complex disease pathogenesis, lack of predictive preclinical models and insufficient validation of pharmacological targets.

In collaboration with Prof. Jonathan Fallowfield (CIR), and Prof. David Hay (CRM) this project will harness human big data (e.g. SteatoSITE Data Commons) to engineer stage-specific in vitro models for non-alcoholic fatty liver disease (NAFLD). NAFLD, affecting 25% of the global population, is the leading cause of chronic liver disease and can progress to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and cancer. To date, there are no approved medicines for NAFLD. The in vitro model developed herein will permit experimental characterisation of NAFLD evolution (dysregulated genes/pathways, potential therapeutic targets) and set the basis on which to build a framework for automated design and efficacy testing of promising therapies.

To engineer the in vitro model, the student will use computational tools for omics data to identify which genes characterise the disease stages at the molecular level. Through synthetic biology techniques, the student will hence build a library of 3D liver organoids ideally suited to detail the dynamics of disease progression, in response to perturbation of the culture environment. High-throughput, long term monitoring of the disease model in response to tightly controlled environments will be enabled via a novel microfluidic chip the student will design. 

This project offers an exciting opportunity to work at the nexus of engineering and medicine to deliver new tools towards precision medicine. The successful candidate will receive multidisciplinary training (e.g. bioinformatics, tissue engineering, microfluidics, computer science). Exposure to SynthSys and cross-college collaborations will provide an ideal ground for the candidate’s career development.

Further Information: 

Lucia.bandiera@ed.ac.uk

This position is open until filled with a suitable candidate

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Closing Date: 

Wednesday, August 24, 2022

Principal Supervisor: 

Assistant Supervisor: 

Eligibility: 

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Applications from students with a background in Biology/Biotechnology, and Engineering are encouraged.

Funding: 

Tuition fees + stipend are available for Home students

Further information and other funding options.