The role of mitochondria in complex diseases such as Parkinson’s Disease and Motor Neurone Disease is now becoming well established, however it is often not known whether altered mitochondrial processes play a causal role in developing disease, or occur as a consequence of them, making it difficult to develop targeted treatment strategies. This project will utilise computational biology and machine learning approaches to both characterise and predict mitochondrial genetic and transcriptional signatures in large population-level datasets (such as UK Biobank) and local disease cohorts, before stratifying cases by identifying the individuals where mitochondrial biology is driving the development of disease. Important and compelling findings will then be analysed further through the integration of multi-omics data (including genetic, transcriptomic and metabolite data) before being taken forward for functional validation.
The student will receive training in computational biology and bioinformatics (including programming), the analysis of high-throughput sequencing data and mitochondrial biology via the Hodgkinson lab. They will also be trained in machine learning techniques application and development, and neurobiology via the Iacoangeli lab.
Objectives:
Year 1: Identify genetic/molecular changes in the mitochondrial and nuclear genomes that influence key mitochondrial processes.
Year 2: Based on the identified genetic/molecular signatures, design and implement machine learning approaches to impute pathogenic mitochondrial processes into population and disease cohorts.
Year 3: Validate predictions via the integration of multi-omics data and functional validation.