Mission and background: I am a systems biologist, and my research program is focused on building predictive quantitative models of microbial community assembly. Microbes inhabit virtually every surface on earth, and they are rarely found alone. Rather, they form complex ecological communities whose composition and collective metabolism has profound implications for biogeochemical cycles, human and animal health, agriculture, and industry. The ultimate goal of my research is to develop a quantitative, predictive understanding of how microbial communities form and how they respond to external and environmental drivers. Our research will reveal rational strategies to design synthetic microbial consortia for biotechnology purposes, and will provide insights into how nutrient shifts and other ecological processes may be used to externally manipulate the composition and function of natural microbial communities. Overview of work in my laboratory: Our work combines computation, experiment, and ecological theory. Experimentally, we monitor the self-assembly of natural microbial communities in well-controlled and defined synthetic environments. The nutrient composition of these environments can be modulated at will, allowing us to quantitatively map the shifts this induces on the taxonomic structure and function of microbial communities. Our results are compared with the predictions of ecological theory, which we have adapted for microbial communities. Finally we use genome-wide computational metabolic models that take the metabolic networks from each species as inputs and predict community metabolism and dynamics. We have been able to show that community assembly follows predictable patterns at the family and functional levels in synthetic environments. These can be understood and interpreted from first metabolic principles, and are consistent with the predictions of ecological theory. Future directions: Over the next five years, we plan to ask how environmental resources quantitatively affect the composition and function of microbial communities. Can we predict community assembly on novel metabolic environments? Can we predict quantitative patterns of carbon source utilization from genomic information? How prevalent are high-order interactions, facilitation, and non-transitive competition in structuring the spontaneous assembly of microbial communities? The computational and experimental methods and systems that I have developed over the past five year as an independent postdoc at Harvard and a tenure track faculty at Yale, uniquely position my lab to address these questions. Our findings will represent a milestone towards developing quantitative, predictive models of microbial community assembly.

Public Health Relevance

My research program seeks to integrate the molecular and ecological scales to quantitatively predict the composition and function of microbial consortia. For that purpose we will combine quantitative community assembly experiments with mathematical ecological theory and explicit genome-wide computational models of microbial metabolism. Our results will provide a roadmap for the bottom-up design of synthetic microbial communities, and they will also provide strategies to manipulate natural communities by modulating their environment.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Unknown (R35)
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Special Emphasis Panel (ZGM1)
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Coyne, Robert Stephen
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Yale University
Schools of Arts and Sciences
New Haven
United States
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