DR IAN OVERTON
Senior Research Fellow
|Research Programme:||Integrative Network Biology|
Ian Overton read Biochemistry at Oxford University. Sitting undergraduate Masters final exams when the first draft human genome was published, Ian was excited by the potential for a step change in medicine and the impetus for computational approaches. He worked as Bioinformatics Officer in industry as part of a gap year that concluded with six-months solo travelling in south east Asia and Australasia. Returning to the UK for doctoral studies in 2001 with Simon Hubbard (University of Manchester, then UMIST) he analysed multiple 'omics datasets, projects included SNP discovery and algorithm development for proteome database generation. Spending time in Tony Whetton's and Stuart Wilson's groups, he validated computational results at the bench. Additionally, he independently developed a collaboration to successfully predict alleles conferring HIV long-term nonprogression, combining protein sequence and structure.
In 2004, Ian began a 5 year postdoctoral position in Geoff Barton's group at the University of Dundee and Scottish Structural Proteomics Facility (SSPF). He developed algorithms for protein crystallisation propensity prediction, and computational pipelines for structural biology target selection; also applied homology modelling to study molecular mechanisms of genetic disease and developed interests in systems-wide network inference and analysis.
Ian moved to an independent position at the MRC Human Genetics Unit in 2009 with a Royal Society of Edinburgh Scottish Government Fellowship. He is employing network biology and machine learning approaches to study phenotypic plasticity in development, metastasis and drug response - towards new clinical tools. He spent sabbaticals at Harvard Medical School dept. of Systems Biology (2012, 2013) and Vanderbilt Medical School, Vanderbilt-Ingram Cancer Centre (2013).
Ian was elected as a founding member of the Royal Society of Edinburgh Young Academy of Scotland (2011), where he chairs a working group on Open Data. He interacts widely to communicate research findings, for example in 2010 he was selected for the Scottish Crucible and has been an active STEM ambassador since 2008.
- 2000, Master of Biochemistry, University of Oxford
- 2005, Doctor of Philosophy, PhD, University of Manchester
Research in a Nutshell
We work to understand how cancer cells can spread around the body (metastasis) and how they become resistant to treatment with drugs; these factors cause the overwhelming majority of cancer deaths. We also develop software to inform clinical decision-making, for example to predict which patients will respond to a particular treatment. Together, these approaches help to develop better and more effective cancer medicine.
We know that cells are organised and controlled by complex interactions between many individual parts (molecules), and so inherently form intricate networks. The properties of these networks underlie virtually every aspect of cell function.
We map and analyse the messages passed, or information flow, amongst molecules by integrating billions of data points that describe key components such as DNA and proteins. Statistical inference, including machine learning, lets the data do the talking in order to reveal the molecular logic that controls health and disease. Indeed, computers are vital to modern biology, which interprets large datasets to gain insight into complex systems.
There is still a lot to discover about what makes the difference between patients that survive or succumb to cancer. However, we have encouraging results in subtypes of renal and breast cancers that may lead to diagnostic tools necessary for personalized medicine and provide direction in the search for more effective treatments.