
Chris Lauber appointed as chair of the ICTV's Nidovirales Study Group
Important position for the head of the Computational Virology research group at TWINCORE

TWINCORE was founded in 2008 by the Helmholtz Centre for Infection Research and the Hannover Medical School. We combine the expertise of medical professionals and scientists from a wide range of disciplines to find answers to the pressing questions in infection research. Our focus: translational research – the bridge between basic science and clinical application.

Important position for the head of the Computational Virology research group at TWINCORE

Two exciting days of science and networking with more than 150 participants

Long-time companion passes away at the age of 73
We conduct translational infection research to improve the prevention, diagnosis and treatment of infectious diseases in humans. We focus on three areas that characterize our research work. Find out here how we proceed and what results we achieve.
Under the leadership of our best scientists, various labs are working on different projects within our research topics.
Bartsch Y, Webb N, Burgess E, Kang J, Lauffenburger D, Julg B
Buttler L, Velázquez-Ramírez D, Tiede A, Conradi A, Woltemate S, Geffers R, Bremer B, Spielmann V, Kahlhöfer J, Kraft A, Schlüter D, Wedemeyer H, Cornberg M, Falk C, Vital M, Maasoumy B
Möhn N, Narten E, Duzzi L, Thomas J, Grote-Levi L, Beutel G, Fröhlich T, Bollmann B, Wirth T, von Wasielewski I, Gutzmer R, Heidel F, Pessler F, Zobl W, Schuchardt S, Ivanyi P, Nay S, Skripuletz T
Patients with chronic rheumatic diseases have an increased risk of infection due to severe inflammation. This project investigates inflammation in various tissues, particularly in systemic sclerosis, in order to develop targeted therapies.
The project investigates how HCMV is recognized by the immune system and which mechanisms the virus uses to camouflage itself. The aim is to understand the immune reactions and develop therapies for severely affected patients.
Population genetic studies show that genetic variability between bacterial strains can influence the evolution of antimicrobial resistance. Using automated laboratory evolution (ALE), we are investigating how genetic backgrounds control AMR evolution.
Thanks to high-throughput sequencing, genome sequences of hundreds of bacterial strains can be analyzed efficiently, revealing differences of up to 60 % in gene content, as in E. coli. With the help of machine learning, we want to better predict the functions of accessory genes and decipher their contribution to survival in specialized niches.

