EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Jack Bradley

Jack Bradley

  • University of Cambridge in collaboration with Dyson Farming

Research Interests

Plant diseases (AMR), Machine Learning, Automation, Agricultural Engineering, Sensing (Electrochemical). 


  • Fabrication of bioreactor module, inline bacterial growth sensing and the inclusion of µcosminline sensing @ The Towards Autonomous Robots and Systems (TAROS) Conference 2023 / CDT Annual Conference / Joint Robotics CDT Conference (September 2023)

About me

I’m Jack and I’ll be joining the CDT programme in October 2022. Before joining the CDT, I graduated with a BSc in Chemistry, with my interests within agriculture. My background is in electrochemical sensors; however, I will be looking into anti-biotic resistance/miniature tuneable bio-reactors. Agriculture is a high impact area, advancement made within this field will impact a great number of people. My PhD studies will be at Cambridge University with Dr Somenath Bakshi, alongside collaboration with other talent individuals within the programme and within relevant areas of research. 

MSc Project

Application of microfluidics and chemical bar-coding to produce a Multi-Input Multi-Output controllers for growth culture tuning

Using microbiological marking of specific proteins that provide useful information for the tuning of the growth culture(s). Chemical dye is attached to the proteins of interest along with marking specific microbial strains.

PhD Project

Bioreactor arrays for analysis and control of food production using genetically modified microbes

Food production using genetically modified algae and bacteria is increasingly becoming a preferable option for high-value products, such as vitamins [1], and could play an important role in future bioeconomy. Compared to traditional agriculture, this new branch offers solutions to several food-security challenges: water pollution, fertilizer use, climate change, land scarcity, etc [2]. Algae and bacteria can be cultured independent of land, without fertiliser, reduced greenhouse gas emissions, [3] and in limited space, which has also recently gained attention for food production in spaceships and on board of the ISS, where spatial constraints are a critical factor. The biomass production capacities of algal and bacterial species are much higher compared to terrestrial plants and can be easily harnessed with genetic modifications. Furthermore, these organisms can be easily and efficiently cultivated in water without fertilisers, making them an environmentally safe, and sustainable choice for the future food production.  

However, several technological challenges still need to be addressed to realise the full potential of genetically modified algae and bacteria in food production [4]. These include: 1) Low genetic stability of genetically modified microbes (GMMs), 2) susceptibility towards contamination, 3) lack of optimisation for genetic design and culture conditions. Cultures of GMM are susceptible to loss of function due to genetic instability or contaminating cells, which lead to reduced production. On the other hand, lack of a systematic approach to optimise GMMs for production rate and functional stability and selecting conditions where the function and functional lifetime is maximised, prevents efficient use of these systems.  

To address these challenges, this proposal aims to develop a feedback-controlled bioreactor array, which could be used to run parallel production and evolution experiments on GMMs to a) select optimised designs for function and functional lifetime and b) to run distributed production experiments where effects of contamination can be minimised through redundancy and continuous harvest of the produced goods. Furthermore, this array will enable easy optimisation of culture conditions and population size to maximize functional lifetime of GMMs for long-term cultivation in an automated manner. 

Accordingly, the specific aims of this project are:  

  1. Engineer a bioreactor array for parallel production and evolution assays.  
  2. Design and calibrate a control algorithm for feedback control of the cultures and product harvest using optical measurements of abundance and composition.  
  3. Analyse genetic stability of GMMs using this setup through functional lifetime analysis. 
  4. Optimise culture conditions and population size to maximise production. 
  5. Perform test production runs with distributed control for continuous product harvest and elimination of contaminated cultures. 

Jack’s PhD project is being carried out in collaboration with Dyson Farming, under the primary supervision of Dr Somenath Bakshi.