PhD Study : Personalized medicine using computational analysis of omics data and molecular interaction networks.

Summary

This project will use computational approaches to improve our ability to use big ‘omics’ datasets to understand differences between patient groups with respect to disease mechanism, disease progression, and drug-response. Stratified medicine (or personalized/precision medicine) depends on the identification of biological features that can be used to separate a patient population into sub-groups, to enable biomarker identification and therapeutic development. “Omics” methodologies such as whole genome or whole transcriptome sequencing, microarrays, and proteomics, are often employed to identify useful biological features, because their high-throughput nature enables the quantification of thousands of features in a single experiment.

One approach to stratification is to apply machine learning / artificial intelligence based algorithms to omics data generated from a large patient cohort in order to build a mathematical model that can correctly assign sub-group identity based on the most predictive biological features. However, this approach ignores another potentially important source of information: the network of known functional relationships between biological molecules. These may take many forms, including gene co-expression, protein-protein binding, miRNA-target and transcription factor to target interactions. Clustering analysis of functional association networks, in which the structural topology of the network is computationally analyzed to identify tightly interacting groups of molecules, has been successfully applied to diverse pathologies including cancer, cardiovascular disease, type 2 diabetes, asthma, and schizophrenia.

The central hypothesis of this project will be that our capacity to stratify a patient population based on high-throughput molecular data is improved by the inclusion of functional association data. At our disposal to test this are: omics datasets and functional association data from public repositories; and in-house datasets from several disease areas currently under study at the Northern Ireland Centre for Stratified Medicine (NICSM).

The candidate will develop computational method(s), which will involve: first to score network clusters based on the extent to which they are impacted at the molecular level in terms of genetic variants or differential expression, followed by deploying (training and testing) various machine learning algorithms to identify clusters whose scores are predictive of a patient’s disease sub-group. Success in the project will feed into the ongoing efforts of the NICSM in these disease areas, focused on biomarker discovery in the short term and therapeutic development in the longer term. In practical terms, the project is intended to develop, test, and implement an analytical pipeline to be built into the NICSM’s analytical platform.

The project will be entirely computational. Thus, we are seeking a student having a strong interest in computational approaches, evidenced by programming skills (such as in Linux/Shell, Python, and/or R), and preferably with knowledge in biomedical sciences, computational biology and/or statistics. However, students from a more biology oriented background but strong interest to learn bioinformatics and programming are also encouraged to apply. Appropriate training will be provided during the course of PhD study.

For any informal enquiry and/or to discuss more about the PhD project, please contact the PhD supervisors: Dr William Duddy (w.duddy@ulster.ac.uk) and/or Dr Priyank Shukla (p.shukla@ulster.ac.uk).

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Completion of Masters at a level equivalent to commendation or distinction at Ulster
• Research project completion within taught Masters degree or MRES
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living