+44 (0)1865 613271
We are interested in understanding how the fly brain develops and functions, as a model for the human brain in health and in disease.
University of Oxford,
South Parks Rd.,
We are interested in understanding how the fly brain develops and functions, as a model for the human brain in health and in disease. We are focusing on elucidating the role of post-transcriptional regulation of gene expression in neural stem cell (neuroblast) development and their differentiation into neurons, as well as in synaptic plasticity during memory and learning. These mechanisms include mRNA transport and localised translation, as well as mRNA stability and processing.
Keywords: mRNA, Neuronal development, Neural stem cells, Drosophila, live cell imaging, synaptic plasticity, neuromuscular junction, brain, neurons, memory, learning, neuromuscular diseases.
We use a wide range of methodologies in our research, from established methods to cutting edge technologies and purpose-built microscopes.
Full list of publications on Pubmed
We welcome informal enquiries from prospective Post-Doc and PhD students
Browse a selection of movies and still images taken using the advanced imaging techniques we use in the lab.
Project Title: Machine learning-based automated hypothesis generation from microscopy images and their associated genome-wide metadata
Supervisor(s) names: Ilan Davis and Stephen Taylor
Project Description: The volume of microscopy images and their associated genome-wide metadata that are generated by many biologists is too large to be effectively browsed and interpreted in order to formulate novel testable hypotheses. Overcoming this challenge is of key importance for future biological discovery as data volumes continue to grow exponentially. The project will bridge this important technological gap in a unique partnership between a biomedical research lab lead by Prof. Ilan and Zegami (https://zegami.com), an Oxford University spinout company. Zegami provides innovative cloud-based software to display vast databases of images sorted interactively in real time with complex metadata. he main aim of the project is to develop machine learning software (using supervised random forest algorithms) that automatically generates scientific hypotheses based on correlations between existing high quality imaging data and genome-wide bioinformatics data, with guidance from the user. For further information please click here
In the Davis lab we have a team of people from diverse scientific backgrounds who bring a wide range of skills to our research.