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Patrick Schloss

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The Schloss lab is broadly interested in beneficial and pathogenic host-microbiome interactions with the goal of improving our understanding of how the microbiome can be used to reach translational outcomes in the prevention, detection, and treatment of colorectal cancer, Crohn’s disease, and Clostridium difficile infection. To address these questions, we test traditional ecological theory in the microbial context using a systems biology approach. Specifically, the laboratory specializes in using studies involving human subjects and animal models to understand how biological diversity affects community function using a variety of culture-independent genomics techniques including sequencing 16S rRNA gene fragments, metagenomics, and metatranscriptomics. In addition, they use metabolomics to understand the functional role of the gut microbiota in states of health and disease. To support these efforts, they develop and apply bioinformatic tools to facilitate their analysis. Most notable is the development of the mothur software package (https://www.mothur.org), which is one of the most widely used tools for analyzing microbiome data and has been cited more than 7,300 times since it was initially published in 2009. The Schloss lab deftly merges the ability to collect data to answer important biological questions using cutting edge wet-lab techniques and computational tools to synthesize these data to answer their biological questions.

Given the explosion in microbiome research over the past 15 years, the Schloss lab has also stood at the center of a major effort to train interdisciplinary scientists in applying computational tools to study complex biological systems. These efforts have centered around developing reproducible research skills and applying modern data visualization techniques. An outgrowth of these efforts at the University of Michigan has been the institutionalization of The Carpentries organization on campus (https://carpentries.org), which specializes in peer-to-peer instruction of programming tools and techniques to foster better reproducibility and build a community of practitioners.

The Schloss lab uses computational tools to integrate multi-omics tools in a culture-independent approach to understand how bacteria interact with each other and their host to drive processes such as colorectal cancer and susceptibility to Clostridium difficile infections.

Kathleen M Bergen

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Kathleen M Bergen, PhD, is Associate Research Scientist in the School for Environment and Sustainability at the University of Michigan, Ann Arbor. Dr. Bergen currently has interim administrative oversight of the SEAS Environmental Spatial Analysis Laboratory (ESALab) and is interim Director of the campus-wide Graduate Certificate Program in Spatial Analysis.

Prof. Bergen works in the areas of human dimensions of environmental change; remote sensing, GIS and biodiversity informatics; and environmental health and informatics. Her focus is on combining field and geospatial data and methods to study the pattern and process of ecological systems, biodiversity and health. She also strives to build bridges between science and social science to understand the implications of human actions on the social and natural systems of which we are a part. She teaches courses in Remote Sensing and Geographic Information Systems. Formerly she served as a founding member of the UM LIbrary’s MIRLYN implementation team, directed the University Map Collection, and set up the M-Link reference information network.

Lydia Beaudrot

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Lydia Beaudrot, PhD, is Assistant Professor of Ecology and Evolutionary Biology, College of Literature, Science, and the Arts, and Postdoctoral Scholar – Michigan Society of Fellows at the University of Michigan, Ann Arbor.

Prof. Beaudrot combines observational data with ecoinformatic and modeling approaches to investigate questions at the interface of ecological theory and conservation biology. The primary goals of my research are to 1) identify the mechanisms that structure ecological communities 2) understand how tropical mammals and birds respond to global change and 3) apply results to biodiversity conservation.

In an era of “Big Data,” in which data-driven decisions are pivotal to modern society, the field of conservation trails behind, with critical decisions based on expert opinion, biased information and irreproducible research. Global conservation targets require long-term monitoring of biodiversity trends, and a new paradigm for how these data are collected, shared and synthesized. Prof. Beaudrot conducts research with the TEAM Network, the Tropical Ecology Assessment and Monitoring Network, which is a partnership between Conservation International, the Smithsonian Institute and the Wildlife Conservation Society. She creates robust analytics to assess biodiversity change and provide scalable solutions for a vital paradigm shift in conservation biology. She is particularly interested in the effects of global change on tropical biodiversity. One of the ways that she assesses this is by monitoring the population status of ~250 mammal and bird species with the Wildlife Picture Index. See wpi.teamnetwork.org.

William Currie

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Bill Currie studies how physical, chemical, and ecological processes work together in the functioning of ecosystems such as forests and wetlands.  He studies how human impacts and management alter key ecosystem responses including nutrient retention, carbon storage, plant species interactions, and plant productivity.   Dr. Currie uses computer models of ecosystems, including models in which he leads the development team, to explore ecosystem function across the spectrum from wildland to heavily human-impacted systems.  He often works in collaborative groups where a model is used to provide synthesis.  

He is committed to the idea that researchers must work together across traditional fields to address the complex environmental and sustainability issues of the 21st century.  He collaborates with field ecologists, geographers, remote sensing scientists, hydrologists, and land management professionals.