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Development of computational and experimental tools for deep annotation of metabolomes

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  • Full or part time
    Prof W Dunn
    Prof M Viant
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

An exciting and challenging 3-year PhD project which lies at the interface of bioanalytical chemistry, bioinformatics and biomedical research is available in the group of Dr Warwick Dunn from April 2016, though a start date to September 2016 will be considered. The project is funded by the BBSRC in collaboration with Thermo Fisher Scientific, a global company operating in the scientific instrument, consumables and service business sectors. Warwick’s research focuses on two distinct and related areas; (1) the development of innovative instrumental and software tools and resources to be applied for non-targeted and targeted studies of mammalian metabolomes and (ii) biomedical/clinical research focused on stratified medicine and understanding the molecular mechanisms of human ageing and diseases associated with endocrinology, immunology and inflammation, cardiovascular diseases, renal diseases and complications of pregnancy. One specific area of research focuses on improved tools and methodologies for comprehensive profiling of complex mammalian metabolomes and in the chemical identification of all metabolites detected; this is the focus of this PhD studentship.

So what is metabolomics and why is it applied? Metabolites act as essential nutrients, building blocks and regulators in many systems. The holistic study of metabolites, defined as metabolomics, provides a dynamic and sensitive picture of an organism’s phenotype and provides high-throughput molecular phenotyping studies which simultaneously screen for thousands of metabolites at a low per sample cost (1). Metabolomics is routinely applied to construct hypotheses in an unbiased manner which are further tested applying integrated ‘omic studies (e.g. metabolomics, proteomics, transcriptomics, epigenetics). Biomedical metabolomics studies are applied to define pathophysiological mechanisms or to identify biomarker panels applied for diagnosis, prognosis or stratified medicine. For an appropriate review see (1).
In non-targeted metabolomic studies data is rapidly acquired providing information on thousands of metabolites, though not all metabolites present are detected and more comprehensive detection of a larger number of metabolites is required. A large number of these studies apply liquid chromatography-mass spectrometry (LC-MS) and commonly acquire retention time, m/z and gas phase fragmentation data to assist in metabolite identification. Metabolite identification is a major bottleneck in metabolomics for all researchers and many biologically important metabolites are not uniquely identified (2).

The research to be performed in this PhD project will focus on developing innovative sample preparation, instrumental and/or computational workflows to increase the volume of biologically important data we acquire and to increase the efficiency in the annotation or identification of all metabolites detected. You will develop and optimise workflows for sample fractionation (applying liquid-liquid extraction and SPE), LC-MS (applying different chromatographic phases) and MSn data acquisition to allow a more comprehensive study of human biofluids and tissues (compared to those currently achievable) to be performed. You will apply computational modelling to predict chromatographic and mass spectral properties in silico to aid our understanding and identification capabilities for those metabolites where no authentic chemical standard is available for robust identification. Finally, you will apply these workflows to study complex mammalian biofluids (e.g. blood) and tissues (e.g. placenta) to validate their appropriateness. The outputs from the research will include robust analytical and computational workflows to enhance our capabilities in acquiring and processing non-targeted metabolomics data and to increase our research outputs in biomedical and clinical research at The University of Birmingham. The research will be performed in collaboration with Prof. Mark Viant and Dr Shan He (School of Computer Sciences) to provide a multi-disciplinary training environment to benefit the student. Members of the Systems Science for Health initiative ( and of the College of Medical and Dental Sciences at The University of Birmingham will also be involved.

The PhD will provide Specialist Training: in LC, MS and extraction chemistry; in metabolomics, and more broadly analytical biomedical sciences; The PhD will also provide Transferable Skills acquired through the extensive courses in the Biosciences Graduate Research School. This training will be truly multidisciplinary to enrich the student experience.

We seek an exceptional candidate with an undergraduate or Masters degree (can be pending) in fields such as bioanalytical chemistry, analytical toxicology or forensics.

For further details on the research in Dunn’s laboratory, including relevant research papers, visit:

Funding Notes

Fully funded studentships are only available to UK nationals (or EU nationals who have been resident in the UK for 3 years).
See BBSRC's requirements at:
These studentships additionally include financial support from Thermo Fisher Scientific.


University of Birmingham metabolomics research:
University of Birmingham – Thermo Fisher Scientific partnership:

1. Dunn WB et al. Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem. Soc. Rev. 2011, 40, 387-426.
2. Dunn, W.B., Erban, A., Weber, R.J.M., et al. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics, 2013, (9, suppl), 44-66.

How good is research at University of Birmingham in Biological Sciences?

FTE Category A staff submitted: 42.80

Research output data provided by the Research Excellence Framework (REF)

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