Kiel Life Science

Forschungsinteressen

John Baines is Professor of Evolutionary Genomics at the Christian-Albrechts-University of Kiel since April 2009. As part of the Excellence Cluster "Inflammation at Interfaces", he has a dual appointment in both the Medical Faculty of the CAU (Institute for Experimental Medicine) and at the Max Planck Institute for Evolutionary Biology in nearby Plön. His working group operates laboratories at both the University Clinic Schleswig-Holstein (UK-SH), Campus Kiel and at the MPI in Plön, serving as a bridge between research in evolutionary biology and medicine. Topics in his group include the evolution of disease genes and the genetic basis of interactions between mammalian hosts and their commensal microbiota, particularly in relation to chronic inflammatory disorders. The lab is a participating member of the International Max Planck Research School for Evolutionary Biology in cooperation with the University of Kiel. John Baines is also a full member of the recently established interdisciplinary center for applied life sciences “Kiel Life Science” (KLS) at Kiel University.

Research Overview: Since being appointed within the DFG Excellence Cluster “Inflammation at Interfaces” in 2009, the group of John Baines has shifted its focus to the coevolution of vertebrate hosts and their complex communities of commensal bacteria, or “microbiota”, in an effort to better understand this relationship in the context of both human health and disease. A combination of population genetics, microbial ecology and evolutionary functional genomics is applied to identify and characterize the complex interactions between hosts, their bacteria and the environment.

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« November 2017 »
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  • 15:00: ZMB Young Scientist Grant Ceremony 2017
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  • 17:00: KON-Seminar (Dirk Schmidt-Arras): The metalloprotease ADAM17 - a decissive signalling scissor in the tumor microenvironment
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  • 20:00: 2. Nachtkonzert im Zoologischen Museum
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  • 17:00: KON-Seminar (Jan-Bernd Hövener): A window into metabolism: Hyperpolarized MR for Oncology
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