Profile

Biography

Julie Wilson is a professor in applied statistics, working on interdisciplinary projects often in partnership with industry. Her research interests lie in the application of mathematical modelling and statistical methods to biological and chemical problems and she is a founder-member of the York Cross-disciplinary Centre for Systems Analysis (YCCSA), an interdisciplinary research collaboration between the University's science departments.

Research

Overview

My research interests lie in the application of mathematical modelling and statistical pattern recognition methods to biological and chemical problems. Collaboration with the Food and Environment Agency, now Fera Science Ltd, has led to projects involving food safety, plant and animal health and environmental issues. We have developed novel chemometric and bioinformatic methods in response to issues arising in various analyses, including the study of drought and disease resistance in plants, the authentication of Manuka honey and the search for biomarkers for Mad Cow disease.

In contrast to the mega-variate data sets obtained by -omics technologies, the analysis of images often involves the extraction of relatively few relevant features from huge numbers of examples. Protein crystallisation is of fundamental importance in structural biology and I have been involved in international efforts to improve crystallisation strategies through automated analysis of images from crystallisation trials. Other imaging projects include the analysis of cell morphology for discrimination of cell types, identification of bladder cancer cells and investigation of cells’ response to drug treatment. 

Industrial collaborators include large pharmaceutical companies, AstraZeneca and Glaxo Smith Kline, as well as small local businesses with projects ranging from quality control of hair dyes to optimisation of maggot diets, with insects being farmed as a sustainable source of protein for animal feed (pigs, chickens and fish).

Research group(s)

Mathematical Biology and Chemistry Research Group

Available PhD research projects

PhD research projects I supervise tend to be interdisciplinary and involve developing new methods to extract useful information from biological or chemical data. Past and current students and their projects include:

Richard Davis, “Analysis of pattern recognition techniques applied to ¹H NMR metabolomics data”.

James McKenzie, co-supervised in Chemistry, “Assessment of the complementarity of data from multiple analytical techniques”.

Jobie Kirkwood, co-supervised in Computer Science, “Analysis of protein crystallization parameters”.

Joanna Simpson, co-supervised in Chemistry and Archaeology, “Investigating the relationship between glutamine deamidation and collagen breakdown in bone”.

Martin Ruscilowicz, co-supervised in Computer Science, “Computational tools for the processing and analysis of time-course metabolomic data”.

Christopher Saunders, co-supervised in Biology, “The role of the chemokine receptor CCR5 during the infection of macrophages by Leishmania parasites”.

George Clarke, co-supervised in Chemistry, “Intelligent synthesis: a unique reaction optimization strategy”.

Teaching

Undergraduate

• Statistical Pattern Recognition
• Practical Data Science with R

Postgraduate

• Statistical Modelling and Practical Data Analysis with R