Mapping the Molecular Biology of Disease: Developing an AI-powered Framework for Assembling Pathway Maps and Models of How Disease Emerges in Healthy Tissue

Summary

Computational models and pathway maps are the backbone of modern systems biology. Encoded in community standards such as SBML (models) and SBGN-ML (maps), they let researchers simulate disease mechanisms, test hypotheses and interpret data.

Yet a stubborn bottleneck remains: when a scientist needs a new model or map, they must manually hunt through existing resources, extract the relevant reactions, reconcile inconsistent annotations, and stitch everything together by hand. It is slow, error-prone work that can take weeks and has to be repeated across labs.

In this PhD we will work to remove that bottleneck. We will develop an AI-powered framework that breaks published models and maps into their constituent reactions, storing each as a richly annotated JSON object with standardised metadata.

From this reusable library, new models and maps can be assembled on demand, powered by Large Language Models that interpret a researcher's need and assemble and connect the right reactions automatically.

We will develop the framework and demonstrate its potential for four biomedically important exemplars that show emergent and topical behaviour: circadian oscillators, cardiovascular disease, acute kidney injury and chronic kidney disease.

These systems will enable us to validate that the models assembled are as trustworthy as those manually curated but can be assembled much more rapidly.

The project sits at the intersection of computational biology and applied AI.

You will gain skills in systems biology, knowledge representation, LLM tooling and reproducible research software, working alongside a team of experimental and computational biologists. It is ideally suited to a numerate, computationally curious applicant from a life sciences, computer science, bioengineering or physical sciences background who wants their work to accelerate discovery across the wider community.

Specific skills required of the applicant for this project include:

Coding; Familiarity with Machine Learning

Important Information: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project within Medicine, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission. If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship. In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project.

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 detailing background, research questions, aims and methodology
• A comprehensive and articulate personal statement demonstrating interest in the subject area
• Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
• Experience using research methods or other approaches relevant to the subject domain

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.

• Applicants should hold, or expect to hold a First Class Honours Degree in a relevant subject area
• Applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to commendation or distinction at Ulster
• Work experience relevant to the proposed project
• Track record of peer reviewed publications
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD scholarships are available for this project to all applicants worldwide, regardless of residency or domicile, and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

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, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

Due consideration should be given to financing your studies.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.