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Biological Sciences

MOSAIC Research Areas

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Microbial communities are complex, adaptive systems in which spatially distributed organisms respond dynamically to changes in environmental conditions and to the abundance or function of other community members.

Most natural ecosystems have high complexity and microbial diversity. The majority of genes, proteins, and metabolites are unknown. That means there are many gaps in our understanding of how these systems drive the cycling of carbon, nitrogen, other nutrients, and energy.

Our main research areas address these knowledge gaps, and they share an overarching goal: a predictive understanding of functional stability in microbial communities.

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Metabolic interactions research targets material and energy flow in microbial communities by identifying the mechanisms of functional specialization. This research links the partitioning and regulation of functions predicted from microbial genomes to the subsequent metabolic interactions relevant to community-level processes.

Specifically, we seek the mechanisms by which carbon and nitrogen cycles are linked in microbial communities and the roles of micronutrients and other environmental variables in governing the rates of these cycles. We conduct the experimental tasks to quantify the rates and forms of carbon and nitrogen cycling under variable environmental conditions. We do this in tandem with metabolic modeling in order to understand the division of labor among members of a microbial community.

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Spatial interactions research explicitly considers how functional specialization influences the ways microbial community members are spatially organized - their "community architecture." This branch of our research also considers abiotic spatial constraints - that is, those from the non-living habitat in which a community lives.

We investigate the relationship between interspecies metabolic interactions and the spatial organization within diverse microbial communities. To do that, we consider environmental parameters, including both local environmental gradients and physical/pore structure. We then formalize these interactions in population-based and interaction-based models in order to predict community functional stability.

Biological Sciences