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

About the Project

This is an exciting PhD project within the Centre for Metabolomics Research at the University of Liverpool. Harnessing network analysis on longitudinal metabolomics data will uncover how bacteria dynamically rewire their metabolism in response to antibiotic stress. This approach offers a powerful strategy to reveal hidden survival mechanisms and guide the development of more effective antimicrobial treatments.

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.

Applications need:

• Cover Letter with a personal statement explaining why you want to do this PhD.
• Cover Letter should include experience in analytical science, biochemistry and or computation.
• Detailed CV
• As the PhD is self-funded: please provide evidence that funding is in place for the full 4 years of the PhD programme.

Funding Notes

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