Significant advances have been made in the field of flow cytometry, which is a core data acquisition and analysis platform for measurement and quality control of Cell and Gene Therapies. Uptake of flow cytometry automated (supervised and unsupervised) data analysis software platforms within the clinical and biomanufacturing sectors is mixed, with a lot of operators still preferring manual gating over automation due to lack of trust and resistance to change.
Recently completed research has investigated the variability of operators when completing manual analysis of flow cytometer data across a range of cell model dimensionality and complexity. Measurement uncertainty toolsets have been uniquely applied and give a more specific definition of variability in these manual analysis scenarios compared to more traditional statistical methods. This has allowed the identification of where measurement variation is potentially introduced, such that continuous improvements can be made to the Flow Cytometer measurement process and hence to the biomanufacture of cell and gene therapies. This research is now to be extended to automated software platforms.
This new research opportunity aims to investigate the application and development of measurement uncertainty analysis to the performance of flow cytometry automated software analysis platforms, (for example SPADE, FLOCK and SWIFT), to understand their variance in specific data analysis scenarios. This provides the foundation for comparison to manual analysis techniques and to the eventual definition of a full measurement uncertainty budget for the Flow Cytometer process. This will better influence how process control can be better optimised for product quality in cell and gene therapies.
The successful applicant will become a member of the growing biometrology group, bridging the gap between metrology and healthcare engineering within the Wolfson School. The applicant will be based in the Centre for Biological Engineering, which has a long history of innovative development and manufacturing research within the field of Regenerative Medicine, and is also an EPSRC Centre of Excellence due to its high impact research.
Please note, this project is unfunded, so access to specific testing software and data will be given, but there is no funding available for stipends or additional laboratory work at this present time.
Supervisors
Primary supervisor: Dr Rebecca Grant
Secondary supervisor: Dr Jon Petzing
Entry requirements for United Kingdom
The candidate should have achieved a 2:1 or 1st class degree (or equivalent international qualification) in a relevant discipline: computer science, biomedical science/engineering, mechanical/manufacturing engineering.
It would be advantageous if the candidate has some prior understanding of the Flow Cytometry technique and measurement uncertainty principles that will be used in this research.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Find out more about research degree funding
Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.
How to apply
All applications should be made online. Under school/department name, select 'Mechanical and Manufacturing Engineering'. Please quote reference UF-RG-2022
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