Developing a platform to improve the identification of therapies for Motor Neurone Disease

University of Sheffield

Identifying the cellular signature of Motor Neurone Disease
Motor Neurone Disease (MND), also known as amyotrophic lateral sclerosis (ALS), is a fatal neurodegenerative disease that causes the nerve cells that control voluntary movement like walking, talking, chewing and breathing to stop working.

There are currently no effective treatments which means one in three people die within one year of being diagnosed and half die within two years of diagnosis. An estimated 333,000 people are living with MND around the world today. In the UK every day, six people are diagnosed with MND and six people lose their lives to MND. One in 300 adults will develop MND.

The causes of MND are unclear. Modelling the disease remains challenging, with research suggesting multiple genetic and environmental factors may be involved. More than 50 genes have been identified as potential key contributors to disease, for example.

Finbar’s project seeks to develop a highly reproducible cellular model of MND, amenable to large scale experimentation, to identify a cellular signature of the disease. Using machine learning and genetic engineering, this signature will be used to investigate genes linked to the disease. Validating these genetics would enable scientists to test potential new treatments for the disease at scale, transforming the fight against MND.

Finbar is a graduate of Imperial College London, having earned his Bachelor’s degree in Biology in 2019. He completed a placement year as part of his degree at AstraZeneca developing high throughput screening for breast cancer research. Finbar has previously worked under the Science, Industry and Translation committee at The Royal Society and is a Fellow of the Zoological society of London.


"In order to treat debilitating conditions like Motor Neurone Disease researchers need accurate and reliable models to experiment on. Through my project with LifeArc and the University of Sheffield we are hoping to develop such a model and investigate genes linked to the disease, so the next generation of therapies can significantly improve health outcomes for future patients"