Funded PhD Opportunity Bioinformatics approach to identifying effective sensitising therapeutics for rheumatoid arthritis

This opportunity is now closed.

Subject: Biomedical Sciences


Rheumatoid arthritis (RA) is a chronic destructive disease of joints, which affects ~ 25 million adults in the world. RA is a painful and disabling disease and presents significant healthcare burden to society. Connectivity mapping (Lamb et al 2006) is an advanced bioinformatics technique that establishes connections among different biological states via their gene expression profiles/signatures. An important application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state, or more generally, altering a disease state towards a more favourable condition, for example, enabling patients to be more responsive to an otherwise ineffective treatment. Our team has undertaken pioneering research in this area, by developing a robust framework for connectivity mapping, releasing tested tools for such high throughput tasks, and successfully applying the developed technologies to different disease areas including cancers and inflammatory diseases [Zhang & Gant 2008, 2009; McArt & Zhang2011; McArt et al 2013; Wen et al 2015, 2016; Ramsey et al 2013;Malcomson et al 2016, PNAS;Wen et al 2017].

In this project, we will apply the connectivity mapping principle to rheumatoid arthritis (RA). This project aims to develop an integrated Bioinformatics and Big Data approach to the identification of sensitising agents that can improve RA patients’ response to biological treatments, such as infliximab (IFX), tocilizumab (TCZ), or abatacept (ABT).  Successful identification of such effective sensitising agents can help to address important unmet clinical needs, and bring great benefits to patients and NHS. Although this project focuses on non-responsive rheumatoid arthritis as a target disease, the approach developed and tested is also readily applicable to other disease areas of stratified medicine.

The Aims of the research project are:  1) To identify suitable public data sets of RA patients with gene expression profiling data (eg, those deposited in public data repository GEO and/or ArrayExpress) and clinical data on their response to standard DMARDs and biological treatments (eg. IFX, TCZ, ABT). 2) Use the multi-omics data and clinical data available in the retrieved public data sets, to identify gene signatures that are characteristic of response to certain biological treatments for RA.  3) To process and analyse the drug-induced gene expression data to construct reference gene expression profiles for FDA-approved drugs and experimental compounds available through the LINCS database.  4) To establish the connections between the IFX-response gene signatures generated in Aim 2 and the reference drug profiles in Aim 3, to identify candidate chemical agents, particularly FDA approved drugs, which have the desirable effects on the specific IFX-response gene signatures and therefore be able to sensitise IFX-treatments and induce remission in otherwise non-responsive RA patients  5) To validate the predicted effects of candidate drugs on model systems of the disease.

This is an interdisciplinary bioinformatics and BigData  research project; it requires an individual with good computational skills and statistical knowledge.  A good understanding of basic biological processes and some experience with basic biological lab experiments would be desirable, but training on these aspects will be provided during the course of PhD study.

Essential Criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • 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
  • Masters at 65%
  • 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


    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £15,009 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,009 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.

Other information

The Doctoral College at Ulster University

Launch of the Doctoral College

Current PhD researchers and an alumnus shared their experiences, career development and the social impact of their work at the launch of the Doctoral College at Ulster University.

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My experience has been great and the people that I have worked with have been amazing

Kieran O'Donnell - 3D printing of biological cells for tissue engineering applications

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Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.

Michelle Clements Clements - MRes - Life and Health Sciences

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Throughout my PhD I’ve been provided with continuous support and guidance by my supervisors and the staff at the University.I’ve also received many opportunities to further enhance my professional development in the form of teaching experience and presenting my work at conferences which will aid in my pursuit of a career in academia or industry.

William Crowe

Key Dates

Submission Deadline
Monday 19 February 2018
Interview Date
6, 7 and 8 March 2018

Contact Supervisor

Dr Shu-Dong Zhang

Other Supervisors

Apply online

Visit and quote reference number #238239 when applying for this PhD opportunity