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Microbial communities, such as those found in the gut microbiome or other body regions, are crucial for the health and development of hosts. However, understanding how these microbes interact with each other – whether they compete, cooperate, or influence each other indirectly – has been difficult to decipher. A new method now sheds more light on this issue: an innovative stochastic approach that allows for more precise analysis of interactions between microbes.
Traditional models for analyzing microbial communities often rely on average microbial abundance over time. While these models can predict temporal changes effectively, they often fail to capture the actual interactions between microbes. An international research team, including scientists from the Max Planck Institute for Evolutionary Biology (MPI-EB), University College London (UCL), and the University of Aix-Marseille, all involved in the SFB "Metaorganisms" at CAU Kiel, has now addressed these challenges and developed a method that goes far beyond this traditional approach. "Our model not only takes averages into account but also incorporates variability and correlations between data points, enabling us to capture microbial interactions much more precisely," explains Dr. Román Zapién-Campos, PostDoc at UCL and lead author of the study.
The researchers developed a stochastic model based on microscopic transition rates – such as birth, migration, or mutations – and calculated the statistical moments of the microbiome composition. This approach allows both the parameters and their uncertainties to be determined. It not only provides more reliable predictions about the dynamics of microbial communities but also enables the detailed identification of specific interactions between microbes.
"Our method bridges a critical gap between metagenomic data and ecological models," says co-author Arne Traulsen, Director of the Department of Theoretical Biology at MPI-EB. Notably, the approach works with both relative abundance data – typically found in metagenomic studies – and absolute abundance data. This significantly broadens its applicability and allows for analyses regardless of the type of data available.
The approach was successfully applied to simulated data, a key test for the reliability of the method. Additionally, a simplified mouse microbiome, consisting of twelve well-characterized species, was analyzed. "We were able to uncover not only the underlying mechanisms of these microbial communities but also to precisely quantify the uncertainties in the model parameters," explains co-author Dr. Florence Bansept, Group Leader and CNRS Researcher at the University of Aix-Marseille.
This study represents a significant advancement in the understanding of microbial ecosystems and their interactions with their hosts. The newly developed method offers promising prospects for better understanding microbiomes in medicine, as well as microbial communities in the environment.
The results were recently published in PLOS Biology and are available to the public. (November 21st, 2024)
Prof. Dr. Arne Traulsen
Dep. of Theoretical Biology
Max Planck Institute for Evolutionary Biology
doi.org/10.1371/journal.pbio.3002913
Parameter inference workflow and microbiome data properties.
MPI EvolBio
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Parameter inference workflow and microbiome data properties.
MPI EvolBio
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