Microbiomes are found in diverse environments, from oceans to the human gut. Changes in the state of microbiomes, driven in part by interactions among large numbers of microbial populations, can influence the health and functioning of humans and the environment and make large-scale prediction and modeling a challenge. Using a novel method based on topological data analysis and network analysis, Libusha Kelly, Ph.D., and colleagues have correlated major types of microbiome compositions with clinical health. Their study was published online on October 14 in npj Biofilms and Microbiomes.

The researchers were able to use their analysis method to distinguish between the human gut microbiome dynamics found in “healthy” and “sick” or incompletely-recovered people, despite the continually changing microbiome composition in both conditions. The findings could help researchers define markers of “healthy” microbiota dynamics on an individual patient level. On a larger scale, the method may also prove useful for monitoring the health of environmental microbial communities in systems such as the ocean or soil.

Dr. Kelly is an associate professor of systems & computational biology and of microbiology & immunology at Einstein. Additional authors on this study include: William Chang, Ph.D., at Einstein and David VanInsberghe, Ph.D., at Emory University.