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Comparison of machine learning techniques using measurement uncertainty for analysis of cell and gene therapy manufacturing data

Wolfson School of Mechanical, Electrical and Manufacturing Engineering

About the Project

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.

Find out more

Links to the Wolfson School of Mechanical, Electrical and Manufacturing Engineering and Centre for Biological Engineering can be found here:

Loughborough University

Loughborough University is a top-ten rated university in England for research intensity (REF, 2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.

Find out more:

Entry requirements

The candidate should have achieved a 2:1 or 1st class degree 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:

How to apply

All applications should be made online. Under school/department name, select 'Mechanical, Electrical and Manufacturing Engineering'. Please quote reference RG-UF-2020.

The deadline for applications is 31 October 2020.

Funding Notes

This is an open call for candidates who are sponsored or who have their own funding. If you do not have funding, you may still apply, however Institutional funding is not guaranteed. Outstanding candidates (UK/EU/International) without funding will be considered for funding opportunities which may become available in the School.

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