Engineering and Modelling Viral Gene Circuits in Bacterial Cells(UG and PG summer project)(apply by 30 April)

Viruses that infect bacteria can be repurposed as programmable biological systems for targeting antimicrobial resistant (AMR) infections, a major global health challenge. These viral infection programs operate through coordinated gene expression modules that reconfigure the host cell. However, predicting and controlling their behaviour remains difficult due to the complexity and variability of gene expression dynamics. 

This project treats viral gene expression as an engineered dynamical system, in which genetic modules act as programmable components whose behaviour can be quantitatively modelled, predicted, and ultimately controlled. Instead of studying full viral infection, individual gene modules will be implemented in bacterial cells using inducible genetic constructs. This enables controlled inputs to the system and systematic measurement of its response. 

The undergraduate will work alongside a PhD student who will establish the experimental platform. The undergraduate’s primary focus will be to develop a predictive computational model of gene expression dynamics, using stochastic simulation to capture variability, timing, and noise in protein production. 

  • The core engineering objective is to develop a predictive model of a modular gene expression system, enabling: 
  • Quantitative prediction of system response to controlled inputs 
  • Identification of key parameters governing system behaviour 
  • Iterative refinement of the model using experimental data (design–build–test–learn cycle) 

By comparing model predictions with experimental measurements, the student will learn how to build and validate models of complex systems and improve their predictive accuracy. 

The long-term aim is to establish design principles for programmable biological systems, including engineered viruses that can selectively target bacterial pathogens. This contributes to the development of next-generation antimicrobial strategies and aligns with engineering approaches to tackling AMR. 

The student will gain transferable skills in computational modelling, simulation of dynamic systems, data analysis, and interdisciplinary engineering, making this project relevant to careers in engineering, data science, and quantitative research. 

Essential Knowledge, Skills, and Attributes 

  • Basic programming experience (Python or MATLAB) 
  • Familiarity with mathematics and simple modelling concepts 
  • Interest in learning how engineering methods can be applied to biological systems 
  • Ability to work independently and engage with a supervisor 

Skills and attributes that would be advantageous

  • Familiarity with gene expression models or biochemical reaction networks 
  • Coursework in control systems, dynamical systems, or statistical inference 
  • Experience analysing experimental data (e.g., curve fitting, parameter estimation) 
  • Interest in interdisciplinary research at the interface of engineering and biology 

Timing

8 weeks (mid-July to mid-September) 

Supporting Information 

General overview of research in supervisor’s lab: https://www.bakshilab.net/research 

Supervisor

Dr. Somenath Bakshi (Associate Professor), sb2330@cam.ac.uk

Recent paper from the supervisor lab which developed the first assay to track virus infection steps in bacteria: https://www.biorxiv.org/content/10.1101/2024.04.11.588870v4.full 

Application Details

Please email Dr Somenath Bakshi, sb2330@cam.ac.uk, with a copy of your CV along with a short statement in your email explaining why you are interested in this particular project.

Deadline for applications: April 30th, 2026