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Education/Academic qualification

Doctor of Philosophy (PhD), University of Manchester
Master of Biochemistry, University of Oxford


Dr. Ian Overton read Biochemistry at Oxford University, including research work in Prof. Dave Stuart's laboratory and a summer internship with AstraZeneca. 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 Prof. 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 Profs. Tony Whetton and Stuart Wilson 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 Prof. Geoffrey 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.

Obtaining a Royal Society of Edinburgh Scottish Government Personal Fellowship in 2009, Ian moved to an independent position at the MRC Human Genetics Unit and secured a Chancellor's Fellowship at the Centre for Medical Informatics from 2015. Ian 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), supported by Marie Curie Actions.

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.


PhD (2005) University of Manchester

MBiochem (2000) University of Oxford

Current Research Interests

Understanding the complex networks that control cell phenotypic plasticity in metastasis and drug resistance, towards more effective and personalised medicine.


Royal Society of Edinburgh Young Academy of Scotland Open Data working group - http://www.youngacademyofscotland.org.uk/our-work/open-data.html

TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data - www.tmanavigator.org


Research Interests

We study molecular control of phenotype with particular emphasis on metastasis and drug resistance. We develop algorithms for patient stratification and analyse high-dimensional datasets, modelling the complex networks that control cell behaviour - towards advances in cancer medicine.

Major interests:

  1. Understanding molecular control and consequences of cell phenotypic plasticity in metastasis and drug resistance.
  2. Developing more effective approaches for cancer patient stratification.
  3. Generation of novel algorithms, techniques and computational workflows to advance the above.

Navigating from molecular measurements to phenotype implies understanding gene function (including gene products and their products). However, many coding genes are poorly characterised, but coordinately regulated (e.g. in differentiation). Furthermore, new functions continue to be discovered even for deeply studied genes, and most noncoding genes are not well understood (e.g. lncRNA, miRNA). Thus, a substantial portion of gene function is uncharted. Data driven networks provide useful abstractions to fill these knowledge gaps, enabling testing and generation of mechanistic hypotheses. One example application is the design of combination therapies to overcome drug resistance.

The spread of cells from a primary tumour to a secondary site remains one of the most life-threatening pathological events. Epithelial-Mesenchymal Transition (EMT) is a cell programme involving loss of cell-cell adhesion, gain of motility, invasiveness and survival; these properties are fundamental for metastasis. Epithelial remodelling is also crucial for development (e.g. gastrulation). Reactivation of a programme resembling EMT is a credible mechanism for key aspects of the invasion-metastasis cascade and an MET-like process may produce the (re)differentiation frequently observed in secondary tumours. Indeed, oncofetal signalling pathways (e.g. Hedgehog, Wnt, TGF-beta) activate EMT, and promote metastasis in multiple cancers.

We have generated probabilistic systems-wide gene networks and are using these to investigate aspects of EMT/MET in different contexts; including to identify new EMT players, pathway crosstalk and drivers of metastasis. We also infer small scale causal networks combining ex vivo immunohistochemical and clinical measurements. These models integrate carefully selected data to represent the specific biological/clinical context of interest, including multiple 'omics datasets. Therefore, our work involves integration of 'big data' with machine learning and graph theoretic/statistical analyses. Supervised as well as unsupervised techniques are employed, including support vector machine and information-theoretic approaches (e.g. conditional mutual information). Prediction performance is assessed by rigorous benchmarking with blind test data.

Novel algorithms are developed where required to advance biomedical understanding, for example we are working on methods towards systems-wide dynamic modelling of renal cancer drug resistance. Tools are made widely accessible (e.g. www.tmanavigator.org).

We collaborate closely with clinical colleagues and aim to translate results into medical practise.


My 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.

Highlighted research outputs

  1. Sunitinib treatment exacerbates intratumoral heterogeneity in metastatic renal cancer

    Research output: Contribution to journalArticlepeer-review

  2. ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction

    Research output: Contribution to journalArticlepeer-review

  3. A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

    Research output: Contribution to journalArticlepeer-review

  4. XANNpred neural nets that predict the propensity of a protein to yield diffraction-quality crystals

    Research output: Contribution to journalArticlepeer-review

  5. TarO: a target optimisation system for structural biology

    Research output: Contribution to journalArticlepeer-review

  6. A normalised scale for structural genomics target ranking: the OB-Score

    Research output: Contribution to journalArticlepeer-review

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Highlighted research activities & awards

  1. Chair, RSE Young Academy of Scotland Open Data working group

    Activity: Consultancy typesContribution to the work of national or international committees and working groups

  2. International Conference on Intelligent Biology and Medicine 2013

    Activity: Participating in or organising an event typesParticipation in conference

  3. Open Science Data Cloud Partnerships for International Research and Education (PIRE) workshop

    Activity: Participating in or organising an event typesParticipation in workshop, seminar, course

  4. National Institute for Medical Research (NIMR) Genetics and Development Seminar Series

    Activity: Participating in or organising an event typesParticipation in workshop, seminar, course

  5. Carlos Lopez

    Activity: Hosting a visitor typesHosting an academic visitor

View all (8) »

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