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Microbial and host interactions in the gut microbiome

School of Biosciences

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Dr J U Kreft , Dr C Tselepis , Dr Richard Horniblow No more applications being accepted Competition Funded PhD Project (UK Students Only)

About the Project

Gut microbiome and health. We have known from poorly germ-free animals that gut microbes are important for the health of the animal or human host for a long time, but only with the recent revolution of next-generation sequencing that drove the surge of research on the gut microbiome have we begun to discover the multitude of ways in which microbes affect us.

Gaps in our understanding. An important issue with sequencing DNA extracted from poo is that the spatial structure in which the various microbes are self-organized in the gut community, facilitating local interactions between neighbours, is completely destroyed. Likewise, many mathematical models to date ignore spatial structure by effectively modelling the gut as a continuous-flow stirred tank reactor (CSTR). We know from our work and other studies how important spatial structure is and also that mathematical models can predict how competitive and cooperative interactions generate certain spatial structures (1).

Approach. There are other issues with current research that can only be overcome by integrating laboratory experiments with animal and clinical studies by using mathematical models as the glue between them. Mathematical models can take data from one system as input to predict dynamics in another system, which can then be contrasted with data from that system. Many challenges arising from the complexity of the gut, consisting of thousands of often poorly studied microbes interacting with each other and the body, which is also highly complex. Models, mathematical and laboratory, help by simplifying this system, but one needs to check the simplifications are not caricatures of the system.

Probiotics. The challenge of complexity is evident from research on probiotics. Most had little success (2) because they were isolated from different habitats and they don’t integrate into an existing community. What is needed is a model-aided design of synthetic multispecies probiotics where different members work together to form a stable community.

We have been developing an agent-based model to simulate the gut microbial community called eGUT for the electronic gut. This work has been funded by NC3Rs and has progressed to a stage where the model can be applied to understand and predict a variety of interactions between microbes and microbes and the host mucosa.

Project aim and objectives. Our long term goal is the validation of our agent-based modelling platform with role model applications in order to build a ‘customer base’ of users of our platform. This will open up lots of opportunities for collaboration with gut microbiome researchers and probiotics companies in the future. The aim of this project is to study the competition of pathogens with commensal or probiotic bacteria in the gut environment using a combination of mathematical and laboratory models of the gut and time permitting animal or human study data from collaborators. As the mathematical model is challenged with experimental data, it will be improved and validated iteratively.

Methods and skills. The project enables learning a wide range of skills and cross-disciplinary working, from computer programming, modelling, data analysis and statistical inference to culturing bacteria and running laboratory models of the gut. Samples can be analysed for specific metabolites or untargeted metabolomics. Quantifying the population dynamics can be done by various methods such as qPCR or sequencing or flow cytometry, depending on the system and the bacteria to be analysed.

Funding Notes

Funded by MIBTP, a BBSRC funded doctoral training partnership. Overseas students are eligible in principle but tuition fees will only be paid up to the level for home students and there is no funding for the shortfall in place.


Hellweger FL, Clegg RJ, Clark JR, Plugge CM, Kreft JU (2016). Advancing microbial sciences by individual-based modelling. Nature Reviews Microbiology 14: 461–471.

Kreft JU (2017). Mathematical modelling of the microbiota for manipulating its membership (Part of editorial series: The microbiome as a source of new enterprises and job creation). Microbial Biotechnology 11: 145–148

Kreft JU, Gülez G (2019). Microbiology and Mathematics: microbiological meaning from mathematical models. Microbiologist (SfAM magazine) 20: 24–29
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