PhD Opportunities In Bioinformatics and Computational Biology

Dr. Georgios Gkoutos – (

A major aim of the biological sciences is to gain an understanding of human physiology and disease. One important step towards such a goal is the discovery of the function of genes that will lead to better understanding of the physiology and pathophysiology of organisms ultimately providing better understanding, diagnosis, and therapy. Our increasing ability to phenotypically characterize genetic variants of model organisms coupled with systematic and hypothesis-driven mutagenesis is resulting in a wealth of information that could potentially provide insight to the functions of all genes in an organism. The challenge we are now facing is to develop computational methods that can integrate and analyse such data.

Research in the Gkoutos Lab aims to achieve the systematic acquisition, objective documentation, integration and analysis of biomedical data at different granularity levels as an essential tool for aiding our understanding of the underlying molecular mechanisms of disease. It employs biomedical resources – from DNA sequences to abnormal phenotypes – to help understand basic biological phenomena and ultimately human disease and how it can be treated. We apply a variety of computational methods from biomedical knowledge representation, management and analysis, comparative phenomics, machine learning, translational research, formal logics, natural language processing, and systems approaches to bioscience. The primary areas of applications include the investigation of the pathophysiology and pathobiology of human disease, diagnostics strategies to support identification of causative genes, drug discovery and gene prioritisation for rare and orphan diseases. Its ultimate aims is to facilitate the rapid translation of basic research results into a clinical setting, taking into consideration human variation, and therefore aiding personalised therapy.

PhD projects are available (some jointly with IBERS, University of Aberystwyth, PDN University of Cambridge and the Bioinformatics Unit, Medical Research Council) in any of the areas listed below:

  • Biomedical Ontologies
  • Phenotype representation
  • Quantitative Data Modelling Behavior
  • Biomedical data representation, integration and translation

Comparative Phenomics (joint PhD Projects with Dr. P. Schofield, Department of Physiology, Development and Neuroscience, University of Cambridge)

  • Cross species data and knowledge integration
  • Candidate disease gene prioritization
  • GWAS prioritization

Systematic genome-wide phenotyping (joint PhD Projects with Dr. A. Mallon, Bioinformatics Unit, Medical Research Council)

  • Animal Models of Disease
  • Anima Models of Behavioral Disease
  • Assay Optimisation

Translational Research (joint PhD Projects with Dr. P. Schofield, Department of Physiology, Development and Neuroscience, University of Cambridge)

  • Rare and orphan diseases
  • Copy number variations
  • Gene function determination
  • Overgrowth Syndromes
  • Dermatology Syndromes
  • Gene expression
  • Diagnostics strategies to support identification of causative genes
  • Assessing the pathogenicity associated with mutations investigation of behavioral and mental diseases as well as their physiological mechanisms.

Pharmacogenomics (joint PhD Projects with Dr. A. Mallon, Bioinformatics Unit, Medical Research Council)

  • Novel drug discovery and repurposing
  • Phenotype data and drug discovery
  • Side effects and drug indications

Systems Biology and Cancer

  • Physiology knowledge representation and integration
  • Modelling of cancer progression and optimal cancer chemotherapy

Plant Phenomics (joint PhD Projects with Prof Doonan, IBERS, University of Aberystwyth)

  • Development of Plant Ontologies
  • Plant Genotype to Phenotype associations
  • Environment representation and associations species-independent ontology-based framework to capture plant phenotypic data

Integrative Modelling for Genomics-based Cancer Classification
Supervisor: Dr Chuan LU (


Data Fusion, Kernel Methods, Genomics, Biological Networks, Cancer Classification

Cancer is one of the most complex diseases involving many different changes in the genome of the tumour cells. These differences may lead tumours of the same pathological characteristics to different clinical outcomes and different therapy responses. The goal of this project is to develop computational models using genomic and clinical data to support cancer diagnosis and prognosis. It will incorporate a priori information extracted from biochemical networks for an improved predictive power, thereby aiding in the development of more effective cancer therapies. Two levels of data integration will be considered here: one is to integrate patient specific genomics data gathered from multiple platforms, and the other is to integrate population-based interactome data derived from various biological networks (e.g. metabolic pathways, and protein-protein interactions). Different integrative modelling techniques will be investigated, with a focus on the application of Bayesian and kernel-based methods. Data sets from readily accessible databases are to be used, including the International Cancer Genome Consortium, the Cancer Genome Atlas, and the Gene Expression Atlas.