SSA:Using machine learning to improve data analysis from complex in vivo datasets:lifespan cellular resolution images of the zebrafish musculoskeletal

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Full title: Using machine learning to improve data analysis from complex in vivo datasets: lifespan cellular resolution images of the zebrafish musculoskeletal and healthy joint ageingThe skeletal system is surprisingly dynamic, undergoing remodelling due to changes in gene expressionand or loading throughout life. Changes to either underpin skeletal and joint diseases and over 15million people in the UK have a musculoskeletal disorder such as osteoporosis or osteoarthritis.Zebrafish are increasingly used as the animal model of choice to study developmental biology and cellbehaviour. They offer excellent genetic tractability along with dynamic in vivo imaging due to theirtranslucency and the potential to use fluorescent reporters to track cells in the whole animal. Ourgroup has made >20 mutant lines of zebrafish carrying mutations in genes that lead to disease statesin humans, along with transgenic lines that allow us to see the cells that make up muscle, cartilage,bone, tendons and the immune system in living fish. We have amassed a large number of 3D datasetsthat contain data that we currently do not fully extract. This project focuses on developing machinelearning strategies to process large, complex, 3D in vivo datasets with the aim of using these todevelop high throughput systems for testing of new clinically relevant genes and in vivo compoundscreening to test new pharmaceutical strategies. The project is highly interdisciplinary offering thechance to combine advanced in vivo skills (CRISPR genome editing, live imaging of transgenicreporters) with computational AI and machine learning approaches to visualise and analyse data. Thesupervisory team has members from both academia and industry. The project would give the studenta highly desirable skill set that is increasingly in huge demand. This project would particularly suit astudent with an interest in biological systems and some experience of programming, ideally in Python.