Available
Project number:
2025_A04
Start date:
October 2025
Project themes:
Main supervisor:
Reader in Developmental Genetics
Co-supervisor:
Dr. Malay Singh (Main, BII, A*STAR)Professor Hwee Kuan Lee (Co-supervisor, BII, A*STAR)
Predicting muscle stem cell function from molecular profiling of tissues using an AI deep learning model
Deep learning tools offer the opportunity to make predictions about complex biological outcomes that cannot be easily extrapolated from experimental manipulations. The goal of this project is to build an AI model that can make predictions about the molecular regulators of muscle stem cells using information about cell behaviour. Muscle weakness in ageing and disease is a costly and debilitating condition that affects quality of life and independent living for many people. Identifying interventions that can enhance muscle function by improving muscle stem cell (muSC) would be greatly beneficial across a wide range of conditions. Cell behaviour offers a rich, spatiotemporal readout of tissue biology that can potentially offer more informative data for predicting outcomes in response to a perturbation and so will form the basis for designing a novel model to predict regulators of muSC function. Models will be developed from time-lapsed videos of muscle stem cells responding to injury, and from spatial maps of gene expression generated from a zebrafish model of regeneration. Deep learning models using computer vision approaches will be developed using time-lapsed movies as input and trained using spatial gene expression as labels. The resultant deep learning model will be used to predict spatial gene expression given a time-lapsed video of cell behaviour. This project builds on the expertise of the Knight and Lee groups in cell and tissue biology, spatial transcriptomics and deep learning this project offers a unique opportunity to explore the utility of AI models for investigating a challenging area of health.
We are now accepting applications for 1 October 2025
How to apply - A*STAR projects
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Key dates - A*STAR projects
- Applications submitted by the closing date of Thursday 30 January 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend interview(s). Interviews are projected to take place in March 2025.
- Project selection will be through a panel interview chaired by CDT Directors at EPSRC DRIVE-Health and relevant supervisors from A*STAR Institute followed by informal discussion with prospective supervisors.
- Successful candidates are required to accept their conditional places by 14 April 2025.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at drive-health-cdt@kcl.ac.uk.
