Exposing dynamics in bacterial responses to antibiotic treatments using metabolomics and network analysis

About this opportunity

The aims of this PhD programme of work if to develop and apply network analysis to longitudinal metabolomics data from bacteria exposed to antibiotics to uncover dynamic metabolic responses and adaptation mechanisms.  By constructing time-resolved metabolic networks, where the nodes represent metabolites and the edges reflect biochemical relationships (e.g., enzymes or mass differences) or correlations,  we will track changes in network topology over time.  This approach enables the identification of key metabolites, pathway shifts, and potential biomarkers of antibiotic response.  Network metrics will highlight critical time points and metabolic transitions linked to antibiotic resistance or susceptibility.  Integrating network analysis with mass spectrometry-based metabolomics (including both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) offers a powerful strategy to elucidate bacterial survival strategies and inform the development of more effective treatments.

Training:

• Roy Goodacre (CMR) will supervise the experimental design for bacteria exposed to antibiotics, as well as oversee the data analysis aspects of the project.
• Howbeer Muhamadali (CMR) will supervise the experimental design for bacteria exposed to antibiotics, as well as the data generation using GC-MS and LC-MS.
• [Yun Xu (CMR) will supervise the data analysis involving multiblock methods and network analysis.]

Papers:

• Ahmed, S., Shams, S., Trivedi, D., Lima, C., McGalliard, R., Parry, C., Carrol, E.D., Muhamadali, H. & Goodacre, R. (2025) Metabolic response of Klebsiella oxytoca to ciprofloxacin exposure: a metabolomics approach. Metabolomics 21: 8.
• Ahmed, S., Albahri, J., Shams, S., Sosa-Portugal, S., Lima, C., Xu, Y., McGalliard, R., Jones, T., Parry, C.M., Timofte, D., Carrol, E., Muhamadali, M. & Goodacre, R. (2024) Clinical application of FT-IR spectroscopy for the rapid identification and discrimination of bacterial and fungal sepsis pathogens isolated from children.  Microorganisms 12: 1415.

Further reading

Further work from our research group is found here: https://biospec.net/publications/  – full text is freely available for all published papers.

Who is this for?

You should have, or expect to a 2i or above in Biochemistry, Chemistry or a related field.  Experience in analytical chemistry and chemometrics/machine learning would be advantageous.

Additional costs

We understand that budgeting for your time at university is important, and we want to make sure you understand any costs that are not covered by your tuition fee. This could include buying a laptop, books, or stationery.

Find out more about the additional study costs that may apply to this project, as well as general student living costs.

Funding your PhD

The project is self-funded.  Therefore, funding needs to include tuition fees, bench fees, living expenses

If you're a UK national, or have settled status in the UK, you may be eligible to apply for a Postgraduate Doctoral Loan worth up to £30,301 to help with course fees and living costs.

There’s also a variety of alternative sources of funding. These include funded research opportunities and financial support from UK research councils, charities and trusts. Your supervisor may be able to help you secure funding.

Contact us

Have a question about this research opportunity or studying a PhD with us? Please get in touch with us, using the contact details below, and we’ll be happy to assist you.