Spatiotemporal Modeling of Microbial Communities

Author(s): Shenhav, Liat | Advisor(s): Halperin, Eran | Abstract: Microbial communities can undergo rapid changes, that can both cause and indicate host disease, renderinglongitudinal microbiome studies key for understanding microbiome-associated disorders. However, moststandard statistical methods, based on random samples, are not applicable for addressing the methodologicaland 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 areconsistent across humans, we developed new methods for modeling the temporal and spatial dynamics ofmicrobial 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 thedeveloping microbial communities of infants and those of adults. We also developed models to deconvolutethe dynamics of microbial community formation. Using these methods, we found significant differencesbetween vaginally- and cesarean-delivered infants in terms of initial colonization and succession of theirgut microbial community (Shenhav et al., Nature Methods 2019) as well as the trajectories of thesecommunities 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 thetemporal nature of the human microbiome from the time of its formation at birth and throughout life.