Student Projects in 2017/18
I currently on sabbatical and am not
I will be available from 1-3pm, and from 4-5pm on Wednesday 15 March, or by appointment on other days.
Analysis of microscopy images
Visualisation of colocalisation of EGF-EGFP with rab5-mRFP in a HeLa cell. Adapted from Pike et al, Methods, 2017.
Through my role in COMPARE - the Centre of Membrane Proteins and Receptors - I have access to a wide range of data from the most advanced microscopy techniques currently available. These techniques allow biology to be studied at the molecular level at ultra-high spatial resolution. Some examples of the techniques we are using include:
- STORM - stochastic optical reconstruction microscopy
- SPIM - selective plane illumination microscopy
- SIM - structured illumination microscopy
- Lattice light sheet microscopy
- Confocal microscopy
- TIRF - Total internal reflection fluorescence microscopy
I am interested in using both classical image processing techniques and modern machine learning techniques to understand these richly informative datasets. Projects in this area are very open-ended and are best suited to highly motivated ambitious students who would like to do in-depth study in the areas of image processing, computer vision and machine learning. There will naturally be a reasonable amount of programming but the main complexity will be in the design of the algorithms. Some mathematics may be involved.
Structure of C3 immune protein. From https://en.wikipedia.org/wiki/Complement_component_3
Machine Learning in Proteomics
Automatically identifying proteins and their post-translational modifications from chemical analysis is a very hard problem on which relatively little work has been done. I have several ideas for how one can attack this question of central importance, drawing on recent advances in the machine learning literature. A related problem is to predict the folding of proteins from their amino acid sequence alone. There have been some interesting approaches to this recently and I have some ideas for how to extend and improve these. These projects are potentially suitable for someone who wants to explore the state of the art in machine learning. The programming should be straightforward, but algorithm development will require you to develop a deep understanding of the latest developments in ML.
Extensible Software Toolkits for Bioimage Data
Based on the latest open standards for data exchange, there are opportunities to develop and distribute open analysis packages for bioimage data. I have done some work on this in the past, in the form of a universal format converter, but would like to turn this into a more complete set of analysis tools in a way that can be easily extended to add new functionality. There are some serious challenges here due to the sheer size of some of the data sets which can run to hundreds of GB for a single image set. I am especially interested in what functional programming languages (especially Scala) have to offer in this domain. These are engineering projects suitable for keen programmers.