Microbiomes are known to play important roles in many aspects of health and disease, and can be strongly affected by such variables as diet, medications and physical stress.
The model is described in a paper published in March 2022 in the journal _Nature Ecology & Evolution_. It is the first such model to be developed for so-called "dynamic structured ecological communities," a category that includes microbiomes.
"Understanding how microbiomes respond to change could lead to new insights into how to treat diseases associated with microbiomes, such as Crohn's disease, irritable bowel syndrome and periodontitis," said Christopher Tarnita, PhD, Assistant Professor of Ecology & Evolution and the College at UChicago, and senior author of the paper.
The model was developed in collaboration with researchers at Imperial College London and Stanford University.
A new approach
Most mathematical models of microbial community dynamics do not take into account the structure of the community, in particular, the division of resources among different groups.
However, Tarnita said, structure plays a critical role in determining how communities respond to change.
The model that the team developed is rooted in competition theory, which says that, given fixed resources, the species with the lowest resource threshold will ultimately drive out all the others.
The researchers added another wrinkle to competition theory, reflecting that in real-world settings some species may have an easier time accessing certain resources than others because they are able to exploit those resources more efficiently or because they have the resources in higher abundance in their immediate vicinity.
Testing predictions
The team tested the model by running simulations in which communities were divided into two groups, one of which had easier access to resources, based on real-world data of species in diverse environments.
Consistent with their expectations, the simulations revealed that the group that had the easiest access to resources increased in abundance and took up the majority of community biomass, at the expense of the other group.
The researchers also tested the model's predictions using real-world data sets of microbial communities from the human gut and oral cavity.
These tests showed that the model could provide accurate predictions about the makeup of a microbial community after a disruption—that is, after the introduction of a new species, the removal of a species or a change in the abundance of a key resource.
Implications for human health
Tarnita said the findings could help researchers develop new strategies for manipulating microbiomes to promote human health.
For instance, the model could help identify microbes that could be introduced into the gut to promote digestive health or reduce inflammation.
"A key insight from our model is that if you target a small number of species that have the easiest access to resources, you could potentially drive large shifts in the overall composition of the community," he said. "Our model could help identify those key targets."
