Building a predictive model of the gut microbiome
There are trillions of individual microbes living in the human gut, representing hundreds of species. This community of microbes (collectively, the “microbiome”) is a dynamic, complex and mysterious entity—and one that has an enormous impact on human health.
Some bacteria are good for us, helping us, for example, to digest our food, while other bacteria make us sick. How these bacteria live and interact as a community is largely a mystery. While traditional microbiological techniques help us to understand the individuals in the community, we need more comprehensive methods to study how the community as a whole interacts.
The research of Matthew Biggs and Steven Steinway brought together two distinct fields: network science and microbiology. Network science is a tool that can be used to effectively analyze large systems, looking at the big picture of how a system such as the microbiome is connected and how changing connections will influence the system as whole.
They aimed to determine which bacteria promote stability in the healthy microbiome and which are responsible for disrupting the microbiome in disease. Given the complexity of the microbiome, the tools of network science allowed them to systematically design and propose treatment strategies for numerous human diseases.
Steinway SN and Biggs MB, Loughran TP Jr, Papin JA, Albert R. Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome. PLOS Computational Biology, 11(6): e1004338.