Modelling the ‘good bacteria’ in our gut
We have all heard about the ‘friendly bacteria’ in our large intestines, which decompose difficult to digest food, and, in doing so, release products that help prevent illnesses like IBS, Crohn’s disease, and colonic cancer. What we want to know is how this microbial community responds to changes in diet and to the pro-biotics (e.g. live yoghurts) and pre-biotics that are marketed to us daily. Can we really manipulate this complex ecosystem in the ways we wish to?
One of the main challenges with this work is that everyone has their own unique gut microbial community, and these are difficult to measure. In addition, some of these microbes are hard to cultivate in a laboratory, which means our knowledge of their behaviour (e.g. how fast they grow and what they want to eat) is limited. Furthermore, the microbial ecosystem in our gut is one of the most diverse ecosystems on Earth. It is quick to adapt to any perturbations, and any modelling has to account for this resilience.
BioSS, in collaboration with microbiologists at the Rowett Institute (Aberdeen University), has developed a dynamic microbial ecosystem model for the human gut. This incorporates our uncertainty about the system and captures some of the system diversity by using a multiple random strain approach (Kettle, et. al., 2015 and 2014). This work is an ongoing project, and the model is continually updated as our knowledge of this system improves. To allow other researchers to use this model and to apply it to other microbial ecosystems, we released this work as an R package called microPop (Kettle, et. al., 2018).
Results from the model have inspired laboratory experiments that have given us more information on previously unknown aspects of the human gut microbial ecosystem. This ongoing integration of model and data means we can continually improve our understanding of the system and, hence, model predictions. We are also in the process of adding more data visualisation tools to the model, along with new ways to analyse / visualise the system via network analysis. As an R package the model is easy to share, allowing multiple people to develop different aspects for a wide range of applications, thus ensuring it remains relevant and up to date.
Kettle H, R. Donnelly, HJ Flint, G. Marion. 2014. pH feedback and phenotypic diversity within bacterial functional groups of the human gut. Journal of Theoretical Biology 342: 62-69. doi:10.1016/j.jtbi.2013.10.015
Kettle H, P Louis, G Holtrop, S. Duncan, HJ Flint. 2015. Modelling the Emergent Dynamics and Major Metabolites of the Human Colonic Microbiota. Environmental Microbiology.DOI: 10.1111/1462-2920.12599.
Kettle H, G Holtrop, P Louis, HJ Flint. 2018. microPop: Modelling microbial populations and communities in R. Methods in Ecology and Evolution, 9(2), p399-409. doi: 10.1111/2041-210X.12873
H Kettle, P Louis, HJ Flint. 2022 Process-based modelling of microbial community dynamics in the human colon Journal of the Royal Society Interface 19 (195), 20220489
This work was done in collaboration with Harry Flint and Petra Louis at the Rowett Institute. It was funded under the Scottish Government's Strategic Research Programme for environment, agriculture and food.