, graduate student, University of California, Los Angeles
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. In a different project, we derived a new nonlinear system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), and addressed a longstanding challenge in the field by showing that relative abundance trajectories predicted by this new method are as accurate as trajectories predicted using the standard Lotka-Volterra model (Joseph, Shenhav et al., in review). 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., in prep.). These models, designed to identify and predict time-dependent patterns, would help us better understand the temporal nature of the human microbiome from the time of its formation at birth and throughout life.