Dufour, Yann

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Dufour, Yann

Assistant Professor

B.Sc. Biology, Virginia Tech
M.Sc. Microbiology, Virginia Tech
Ph.D. Microbiology, University of Wisconsin – Madison
Postdoctoral studies, Yale University

Department of Microbiology and Molecular Genetics
6180 Biomedical Physical Sciences
Michigan State University
East Lansing, MI 48824
 Phone: (517) 884-5400
Lab Webpage

Collective behavior in bacterial communities

Bacteria have sophisticated individual and social behavior. They are able to travel toward favorable environments and establish organized communities. My laboratory investigates the elemental rules that govern emergent group behaviors leading to the successful establishment of bacterial communities inside a host or in open environments. We are particularly interested in the relationships between bacterial chemotaxis, cell-to-cell communication, and the early stage of biofilm formation.

Individual cells in clonal populations exhibit considerable phenotypic heterogeneity even in homogeneous conditions because of random fluctuations at the molecular level. One of our research focus is to characterize the potential benefits or disadvantages of individual diversity for collective behavior. Stochastic fluctuations often interferes with the robust performance of biochemical networks but, on the other hand, the resulting phenotypic diversity can provide a selective advantage through bet-hedging or division of labor in bacterial communities.

My laboratory takes a systems biology approach using tools from molecular biology, microfluidics, and microscopy. We design microfluidic devices to investigate how bacteria navigate through mazes and meshes in the presence of chemical gradients. We use video microscopy to characterize the individual behavior of motile bacteria within a population and understand how different phenotypes affect performance in various navigation challenges. We also use detailed simulations to explore the range of conditions within which phenotypic diversity is likely to be beneficial and use our predictions to guide experimental design.

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