Glycans are the least understood biological macromolecule family. Investigation of mammalian glycan functions is often hampered by our inability to incisively change glycan structures generated in the cells and biomolecules under study. This research project aims to develop a new in vivo glycan engineering method that will allow custom shaping of the glycan structures during biosynthesis akin to protein mutagenesis. You will be performing computational simulations using experimentally determined mammalian glycan structure distributions (aka glycan profiles) to complement the efforts of an experimentalist postdoc in York and chemists, biochemists and analytical scientists in Leeds, Oslo and Vienna to make in vivo glycan engineering a reality. The computational work will involve the fitting of simulated mammalian glycan profiles to experimental data using a proprietary object-oriented JAVA code equipped with a Python-based GUI, running stochastic simulation and approximate Bayesian fitting. Your work will adapt and implement the modelling method to accommodate the glycosylation inhibitors designed by chemists and applied and analysed by the cell biologists and analytical scientists. You will then use the trained model to generate predictions of glycan engineering strategies that will be experimentally tested.
You will be highly motivated to plan and perform the most appropriate computational experiments using our in-house code, and skilled to produce excellent results. You will be able to interpret computational results to progress fitting to experimental data while also refining the code as appropriate. You will have excellent communication skills to liaise with the different team members across the UK and Europe. Good communication with other members of the Computational Biology group as well as the Cell Biology lab in York will be essential for success of the project.
You will have well developed coding skills and a willingness to learn, or an existing interest in glycobiology, but some training in specialist skills and knowledge will be provided. We welcome applications from candidates with a strong mathematical or computational background with evidence of their application in other interdisciplinary areas. Good communication skills as well as data analysis and collaborative skills are essential. Previous experience working on an interdisciplinary project is a distinct advantage.