PhD Studentship: Programming Industrially Robust Conjugative Plasmids to Halt AMR Gene Spread Using Large Language Model Genome Design Tools

Supervisors:

Dr Darren N. Nesbeth

Prof Eli Keshavarz-Moore

Abstract:

Horizontal gene transfer via conjugative plasmids is a major but underexploited driver of antimicrobial resistance (AMR) spread. These mobile genetic elements autonomously transfer between bacteria, disseminating resistance genes across microbial populations. Recent advances have demonstrated the potential of “counter-plasmids” that can propagate through bacterial communities while deactivating AMR genes. However, current designs are limited by scalability and complexity. This project aims to overcome these limitations by integrating large language model (LLM)-based genome design tools with bioprocess engineering to create next-generation therapeutic conjugative plasmids. These engineered plasmids will be optimised for industrial-scale production and capable of suppressing AMR gene dissemination in clinical and environmental settings.

Approach and Methods:

• Synthetic biology: Construct modular conjugative plasmids with engineered payloads targeting AMR gene suppression
• AI-driven genome design: Use LLM tools (e.g. PlasmidGPT, Evo2) to refactor plasmid genomes for enhanced manufacturability, safety, and performance
• Microbial validation: Test plasmid efficacy against WHO-priority AMR gene analogues in relevant bacterial hosts
• Bioprocess optimisation: Develop high-cell-density cultivation protocols in bioreactors, addressing plasmid stability, host stress, and yield bottlenecks
• Data-driven design iteration: Integrate empirical data to refine AI-generated plasmid designs

Impact and Outlook:

This project will deliver scalable, deployable countermeasures against AMR by engineering conjugative plasmids that can suppress resistance gene spread. Mid-term applications include GMP-grade oral formulations as adjuncts to antibiotic therapies. Long-term, environmental deployment could mitigate AMR in agriculture and wastewater systems. The integration of AI with synthetic biology and bioprocess engineering represents a transformative approach to tackling AMR at scale.

Training and Student Development:

The student will gain interdisciplinary training in:

• Advanced molecular cloning, CRISPR-Cas systems, and genome engineering
• Bioreactor operation and process optimisation for plasmid production
• Application of LLMs to biological sequence design
• Microbial genetics and horizontal gene transfer
• Cross-disciplinary collaboration across synthetic biology, AI, and bioprocessing

This training will prepare the student for careers in biotechnology, pharmaceutical manufacturing, or academic research.

Research Environment:

The project is hosted in UCL Biochemical Engineering, with collaboration across synthetic biology, computational biology, and microbiology. The student will work within a dynamic, interdisciplinary team with access to state-of-the-art facilities and mentorship from leaders in genome engineering and bioprocess development. Opportunities for collaboration with the National Physical Laboratory (NPL) and other partners will support translational impact.

Desirable Prior Experience:

• Background in synthetic biology, molecular biology, or biochemical engineering
• Familiarity with genome editing, plasmid biology, or microbial systems
• Interest in AI applications in biology and AMR mitigation

How to apply

This project is offered as part of the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste  

Applications should be submitted via the application page on our website  by 12^th January 2026.

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