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Spatiotemporal Modeling of Microbial Communities

  • Author(s): Shenhav, Liat
  • Advisor(s): Halperin, Eran
  • et al.
Abstract

Microbial communities can undergo rapid changes, that can both cause and indicate host disease, rendering

longitudinal microbiome studies key for understanding microbiome-associated disorders. However, most

standard statistical methods, based on random samples, are not applicable for addressing the methodological

and statistical challenges associated with repeated, structured observations of a complex ecosystem.

Therefore, to elucidate how and why our microbiome varies in time, and whether these trajectories are

consistent across humans, we developed new methods for modeling the temporal and spatial dynamics of

microbial communities. We developed a method to identify ‘time-dependent’ microbes (Shenhav et al.,

PLoS Computational Biology 2019) and showed that their temporal patterns differentiate between the

developing microbial communities of infants and those of adults. We also developed models to deconvolute

the dynamics of microbial community formation. Using these methods, we found significant differences

between vaginally- and cesarean-delivered infants in terms of initial colonization and succession of their

gut microbial community (Shenhav et al., Nature Methods 2019) as well as the trajectories of these

communities in the first years of life (Martino*, Shenhav* et al., Nature Biotechnology). These models,

designed to identify and predict time-dependent patterns, will help researchers better understand the

temporal nature of the human microbiome from the time of its formation at birth and throughout life.

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