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Event Detail (Archived)

It’s about Time: Ecological and Eco-evolutionary Dynamics across the Scales

  • This event already took place in March 2024
  • Carson Family Auditorium (CRC)

Event Details

Center for Studies in Physics and Biology Seminars
Liat Shenhav, Ph.D., assistant professor, New York University
Speaker bio(s)

Complex microbial communities play a vital role across many domains of life, from the female reproductive tract, through the oceans, to the plant rhizosphere. The study of these communities offers great opportunities for biological discovery, due to the ease of their measurement, the ability to perturb them, and their rapidly evolving nature. Yet, their complex composition, dynamic nature, and intricate interactions with multiple other systems, make it difficult to extract robust and reproducible patterns from these ecosystems. To uncover their latent properties, I develop models that combine longitudinal data analysis and statistical learning, and which draw from principles of community ecology, complexity theory and evolution. I will briefly present methods for decomposition of microbial dynamics at an ecological scale (Shenhav et al., Nature Methods; Martino & Shenhav et al., Nature Biotechnology). Using these methods we found significant differences in the trajectories of the infant microbiome in the first years of life as a function of early life exposures, namely mode of delivery and breastfeeding. I will then show how incorporating eco-evolutionary considerations allowed us to detect signals of purifying selection across ecosystems. I will demonstrate how interactions between evolution and ecology played a vital role in shaping microbial communities and the standard genetics code (Shenhav & Zeevi, Science, Liao & Shenhav Nature Comm.). Inspired by these discoveries, I am expanding the scope beyond the microbiome, modeling multi-layered data on human milk composition. I will present results from an ongoing study in which I am building integrative models of nasal, gut and milk microbiota, combined with human milk components, to predict infant respiratory health. I found that the temporal dynamics of microbiota in the first year of life, mediated by milk composition, predict the development of chronic respiratory disease later in childhood. These models, designed to identify robust spatiotemporal patterns, would help us better understand the nature and impact of complex ecosystems like the microbiome and human milk from the time of formation and throughout life.

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