We are currently recruiting for the below project, to start in October 2021. More details on the application process and how to apply can be found here. The deadline for applications is Sunday 4th July 2021.
Stochasticity-resilient control of microbial communities in bioprocessing
- Lead supervisor: Dr Duygu Dikicioglu (University College London)
- Co-supervisor: Prof. Gary Lye (University College London)
Cooperation and competition between microbial species that share the same environment drive niche-specialisation and higher-level community organisation. In contrast to natural microbial communities, which have been used in biotechnology processes, including fermentation, waste treatment, and agriculture, for millennia, synthetic microbial communities are well-defined and have reduced complexity. Engineered communities are increasingly finding their way into diverse biotechnological applications, including the bioproduction of medicines, biofuels, and biomaterials from inexpensive carbon sources. Microbial biotechnology benefits from consortia due to the unique catalytic activities of each member, their ability to use complex substrates, compartmentalization of pathways, and distribution of molecular burden. Furthermore, the construction of the microbial consortia is enhanced by computational models through the prediction of preferred metabolic cross-feeding networks and inference on population dynamics over time.
Recent work on perturbations on microbiomes demonstrated that many dynamic transitions follow stochastic rather than deterministic paths, and therefore result in shifts to/from unstable and highly variable community states. The term the ‘Anna Karenina principle’ was coined to describe this argument, based on the quote from Leo Tolstoy that “all happy families look alike; each unhappy family is unhappy in its own way.” This notion highlights a major obstacle in the successful utilisation of microbial communitites in well-controlled bioprocess settings.
This project aims to address the above stated challenge by discovering metabolic identifiers that withstand the stochastic variations in synthetic community structures, which will allow robust and tuneable control in a bioprocess setting. Community metabolic networks will first be investigated by stochastic metabolic modelling; the candidate metabolic markers identified by modelling will then be used to design a platform for monitoring metabolic markers during growth. The high-throughput platform will allow sufficient variability in community structures to monitor the response to stochasticity. Promising named markers will then be used to implement control actions for a benchtop fermentation process that is designed for the synthetic community. The analyses will be carried out on synthetic (i) mixed bacterial and yeast communities of suspended cells, and (ii) mixed bacterial communities of immobilised cells in biofilms. The analysis of different community structures will inform on the generalisability of the findings.
This project will be based at University College London.