New computational modeling method helps to find out various microbial types present in a person's gut and how the microbial community changes over time. It was developed by Liat Shenhav, Leah Briscoe and Mike Thompson from the Halperin lab, University of California Los Angeles, and colleagues at the Mizrahi lab at Ben-Gurion University, Israel who presented their findings in PLOS Computational Biology.
The types and relative amounts of microbes found in a person's gut can reflect and affect the state of their health. Knowing how this microbial community composition changes over time could provide key insights into health and disease. However, it is unclear to what degree the microbial community composition of a person's gut at a given moment determines its future composition.
To address this question, Shenhav and colleagues developed Microbial community Temporal Variability Linear Mixed Model (MTV-LMM), a new method for modeling temporal changes in the microbial composition of the gut. When tested against real-world data, the new tool makes more accurate predictions than do other models previously developed for the same purpose.
Looking forward, MTV-LMM could be applied to explore temporal dynamics of the gut microbiome in the context of disease, which could lead to improved diagnosis and treatment. It could also be useful for understanding other types of temporal microbiome processes, such as those occurring during digestion.
"Our approach provides multiple methodological advancements, but this is still just the tip of the iceberg," Shenhav says. In the future, she and her colleagues will work to further improve the prediction accuracy of the model and explore additional applications. "Modeling the temporal behavior of the microbiome is a fundamental scientific question, with potential applications in medicine and beyond."