Iain Styles is a Senior Lecturer in the School of Computer Science at the University of Birmingham and Turing University Lead for the university. His research interests are in the development of new computational methods for understanding complex biological experiments, with current interest centred on studying the biological role, dynamics, and structure-function relationship of proteins using techniques including single molecule microscopy, mass spectrometry, and alanine scanning mutagenesis. His methodological approaches are drawn from classical image analysis, statistical machine learning, and computational topology which he combines with classical simulation techniques such as molecular dynamics to build biologically interpretable models from large-scale data.
He is currently the co-Director of the EPSRC-funded Centres for Doctoral Training in Physical Science for Healthcare, and Deputy Director (Birmingham) of COMPARE - the Centre of Membrane Proteins and Receptor, an interdisciplinary research centre established jointly by the Universities of Birmingham and Nottingham to develop novel methods for studying single membrane proteins. His research has been supported by funding from EPSRC, the British Heart Foundation, the European Union, and the Dunhill Medical Trust. He was awarded his PhD in Theoretical Condensed Matter Physics from Birmingham in 2003.
Iain Styles’ current research interests are in developing new techniques for understanding high-content experimental data in discovery-driven biology. A major current focus is to take advantage of the central organising principle of compositionality in biology and to construct compositional representations of data that reflect the underlying biological structure. Such a representation would allow us to answer fundamental questions: What are the constituent parts? How are they organised with respect to each other? How does that organisation change over time?
Dr Styles is currently developing new methodological approaches to construct these models that draw on ideas from topology, statistical machine learning, and hypergraph theory. The testbeds for the new methods will be i) mass spectrometry imaging, where we aim to extract parts that correspond to chemical species and, compositions of parts that correspond to different tissue sub-types and ii) single molecule microscopy, where parts correspond to individual protein species, and their compositions to different components of the cell’s structure.