Advances in Automation to Aid Metabolomics Analysis Using NMR

About the Project

We are recruiting a PhD student to integrate data-driven methods with NMR spectroscopy to enhance the characterisation of cell culture media and metabolites, increasing throughput and reducing manual intervention. This is an EPSRC funded Industrial Doctoral Landscape Award PhD project and is co-supervised by Dr. Christian Ludwig at the University of Birmingham and Dr. Caitlin Evans at GSK. The main part of the project will be done at the University of Birmingham, but the student will also have the opportunity to be based at GSK for a minimum of 3 months.

This interdisciplinary approach will involve experimental optimisation, leveraging computational tools, statistical modelling, and emerging AI/ML applications to streamline and accelerate the workflow for complex mixtures and metabolomics samples.

All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048, https://github.com/ludwigc/metabolabpy, https://github.com/ludwigc/qtmetabolabpy, https://github.com/ludwigc/metabolabpytools). Therefore, this research project requires the student to up-skill their Python knowledge, and to be confident in writing and implementing code alongside extracting information, trends, and patterns from large datasets.

Topics to explore during this PhD project include:

• Investigating available software options
• Methods to semi-automate or fully automate quantification of experimental data with relevant checks for robustness, accuracy, and precision.
• Alternate pulse sequences/acquisition parameters to increase throughput without sacrificing data quality, such as 2D NMR
• Application of chemometrics to increase the amount of information available within a given sample set.
• How use of AI/ML to further advance steps of the workflow.

While the majority of the project is computer based, there is a small lab-based component inorder to generate cell samples to be able to acquire the NMR data.

Once proof of concept has been demonstrated successfully at Birmingham on commercial media samples, the methodology can be tested on real samples either commercially available or from within GSK.

Funding Notes

This is an EPSRC funded Industrial Doctoral Landscape Award. Funding is available for Home (UK) students, covering fees and providing a stipend at UKRI rates (current stipend: £21,006 p.a.) plus an additional £3000 industrial top-up for 4 years. The PhD project would suit a candidate with an undergraduate degree (2:1 minimum) in computer science, bioinformatics, physics, or chemistry.