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Quantitative Bioscience at the University of Tennessee

Microbial Ecosystem Modeling

Microbe image.

Microbes are found in almost all aquatic and terrestrial environments and are associated with all species of plants and animals. Microbes function in complex communities, from dozen of species in extreme environments to many hundreds or tens of thousands per gram of soil. The work of microbial ecosystem modeling is to understand the underlying mechanisms that determine ecosystem function in microbial communities. Analysis of microbial communities could include, for example, quantification of fluxes through pathways for nutrient resources and energy, identification of interactions of populations with each other and their environment, and inference of higher-order system properties. Mathematical models can provide a deep understanding of microbial community dynamics.

Researcher Department Research Interests
 photo. Michael J. Blum
Ecology & Evolutionary Biology Aquatic ecology, socioecology, sustainability, conservation biology
A. Buchan photo. Alison Buchan
Microbiology Molecular microbial ecology, bacterial aromatic compound catabolism, marine microbiology
T. Hazen photo. Terry C. Hazen
Governor's Chair for Environmental Biotechnology; Civil & Environmental Engineering; Earth and Planetary Sciences; Microbiology Environmental microbiology, systems biology approach to bioremediation, bioenergy and water quality, genomics
S. Kivlin. Stephanie Kivlin
Ecology & Evolutionary Biology Ecosystem ecology, microbial ecology, global change
F. Loffler photo. Frank Löffler
Governor's Chair for Microbiology and Civil and Environmental Engineering; Microbiology Environmental microbiology and microbial ecology, microbial bioremediation, genomics
D. Talmy. David Talmy
Microbiology Ocean microbial ecology & biogeochemistry
S. Wilhelm. Steven Wilhelm
Microbiology Molecular ecology of viruses, bacteria and algae in oceans and large lakes

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From 2008 until early 2021, NIMBioS was supported by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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