The human gut microbiome refers to a complex ecosystem of thousands of microbial species and their genes contained within the gastrointestinal tract. How these organisms behave and the extent to which they are in balance is a key influencing factor upon overall health and in relation to different metabolic conditions.
Unraveling key data is important for health fields and for new drug discovery, yet the process of doing so is complex. Finding solutions for conducting big data analytics and then drawing predictions is important if necessary research is going to progress at a rate that is useful for society. A research team from the Carnegie Institution for Science have constructed a detailed mathematical framework that can help to characterize the ecology of the human microbiome, down to the specific microbiome relating to an individual person.
To assess the potential evolutionary trajectory of an organism, biologists assess the interactions between genes to see which combinations are most ‘fit’. It follows that an organism that is evolving takes the most fit path, according to a biological theory called the ‘fitness landscape‘. This path can be modeled through mathematical techniques.
The researchers took such models and looked at whether this model for genes can be applied to the microorganisms that make up the human microbiome, focusing on the human gut.
Discussing this, lead researcher Will Ludington explains: “If we understand the interactions, we can make predictions about how these really complex systems will work in different scenarios. But there is a lot of complexity in the interaction networks due to the large number of genes or species. These add dimensions to the problem and make it tricky to solve.”
To meet this challenge, the researchers applied a slightly different mathematical framework to assess microbiome data, focusing on capturing the patterns of interactions within a given landscape – the gastrointestinal tract.
Because of the vast range of species that form the human microbiome, the researchers developed their model using the fruit fly. The next step is to test how the model holds up to alternate landscapes, including humans.
The new model is outlined in the Journal of Mathematical Biology, with the research paper titled “Cluster partitions and fitness landscapes of the Drosophila fly microbiome.”
