Telephone:

+44 (0)1865 613271



email:

darragh.ennis@bioch.ox.ac.uk

We are interested in understanding how the fly brain develops and functions, as a model for the human brain in health and in disease.

Research Focus


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.

We welcome informal enquiries from prospective Post-Doc and PhD students


The research in our lab is generously funded by Wellcome, the Leverhulme Trust, the BBSRC and Marie Curie. 

Ilan Davis Lab


Biochemistry Department, University of Oxford

  1. Yang L., Titlow J., Ennis D., Smith C., Mitchell J., Young F.L., Waddell S., Ish-Horowicz D., Davis I. (2017) Single molecule fluorescence in situ hybridisation for quantitating post-transcriptional regulation in Drosophila brains. Methods. pdf
  2. Yang L., Samuels T.J., Arava Y., Robertson F., Järvelin A.I., Yang C-P., Lee T., Ish-Horowicz D., Davis I. (Under Review, Cell Reports) Regulating prospero mRNA Stability Determines When Neural Stem Cells Stop Dividing. pre-print
  3. Yang C-P, Samuels TJ, Huang Y, Yang L, Ish-Horowicz D, Davis I, Lee T. (2017). Imp/Syp Temporal Gradients Govern Decommissioning Of Drosophila Neural Stem Cells. Development. 144(19):3454-3464 pdf. ​
  4. Hailstone M., Yang L., Waithe D., Samuels T.J., Arava Y., Dobrzycki T., Parton R.M., Davis I. (Under Review, Dev Cell) Brain Development: Machine Learning Analysis Of Individual Stem Cells In Live 3D Tissue. pre-print
  5. Moore S., Jarvelin A.I., Davis I., Bond G.L., Castello, A. (2017) Expanding horizons: new roles for non-canonical RNA-binding proteins in cancer. Current Opinion in Genetics and Development. (In press)
  6. Titlow JS, Yang L, Parton RM, Palanca A, Davis I.​​ (2017) Super-Resolution Single Molecule FISH at the Drosophila Neuromuscular Junction.​ Methods Mol Biol. 1649:163-175. pdf

                      Full list of publications on Pubmed

Browse a selection of movies and still images taken using the advanced imaging techniques we use in the lab.

We wish to thank the generosity of the Drosophila research community and acknowledge the contribution of FlyBase and the various stock centres

ILAN DAVIS LAB
OXFORD


our Research

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In the Davis lab we have a team of people from diverse scientific backgrounds who bring a wide range of skills to our research.

Contact Us



Address:


Biochemistry Dept.,

University of Oxford,

South Parks Rd.,

Oxford,

OX1 3QU

Latest News - DPhil Position  2018

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