As informatics manager for the Division of Computational and Systems Medicine, I lead the team responsible for development and implementation of the research software and databases that underpin the Division's research.
Additionally I lead the teams in the MRC-NIHR National Phenome Centre, and Imperial BRC Clinical Phenotyping Centre, responsible for the statistical and computational aspects of both centres data-acquisition programs, monitoring the ongoing quality control, maintaining the laboratory information systems that manage the work flow, and conducting statistical analyses of the datasets generated.
My previous positions include the EU FloriNASH project, investigating the link between gut microflora and the progression from normal function to non-alcoholic fatty-liver disease (NAFLD) and non-alcoholic steatosis (NASH) in clinical patients. My work involved the profiling of the structure of clinical cohorts, and integration of metabolite-profiles, transcriptomic and proteomic datasets.
While studying for my PhD in Computational Medicine, titled Novel Computational Approaches to Characterising Metabolic Responses to Toxicity via an NMR-based Metabonomic Database (pdf avaible here), I also took the role of Data-Curator for the Consortium for Metabonomic Toxicology (COMET) project. In this capacity, I ensured the quality of the processed data as well as designing and implementing software to assist in the analysis of the COMET dataset.
Previous to my time at Imperial College, I studied Biochemistry at the University of Sussex. While at Sussex I undertook an undergraduate project in X-ray crystallography, and succeeded in growing the first X-ray quality crystal of the human protein Phosphoglucosse Isomerase, the structure of which I assisted in the elucidation of, and which is available from the PDB with the code 1IAT.
Upon completing my BSc I undertook an MSc in Bioinformatics at Birkbeck College, London. During my time at Birkbeck my project work specialised in database manipulation and integration, with special reference to the Gene Ontology database and FlyBase genome repository.
et al., 2016, Power Analysis and Sample Size Determination in Metabolic Phenotyping, Analytical Chemistry, Vol:88, ISSN:0003-2700, Pages:5179-5188
et al., 2016, Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping., Anal Chem, Vol:88, Pages:9004-9013
et al., 2014, Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping, Analytical Chemistry, Vol:86, ISSN:0003-2700, Pages:9887-9894
et al., 2013, Weaning diet induces sustained metabolic phenotype shift in the pig and influences host response to Bifidobacterium lactis NCC2818, Gut, Vol:62, ISSN:0017-5749, Pages:842-851
et al., 2010, Ultra Performance Liquid Chromatography-Mass Spectrometry Profiling of Bile Acid Metabolites in Biofluids: Application to Experimental Toxicology Studies, Analytical Chemistry, Vol:82, ISSN:0003-2700, Pages:5282-5289