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Network Medicine — From Protein-Protein to Human-Machine Interactions

Jörg Menche
Monday, November 23, 2020, 2:00 pm – 3:00 pm

Please register for this event under the link provided on the right. We will send the link to registered attendees 1 hour before the event starts.

ABSTRACT / From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a holistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. In my presentation, I will provide a broad overview of how network science can help us understand the pathobiological processes of human disease and present our vision of a virtual reality platform for the next generation of network-based data integration and exploration.

BIO / Jörg Menche studied physics in Germany and Brazil and obtained a PhD at the Max Planck Institute for Colloids and Interfaces in 2010. He worked as a postdoc at Northeastern University and Harvard Medical School in Boston, before starting his own research group at the CeMM Research Center for Molecular Medicine in Vienna in 2015. In 2020 he became full professor at the University of Vienna where he holds a shared appointment at the Center for Molecular Biology (Max Perutz Labs) and the Faculty of Mathematics. His interdisciplinary team combines backgrounds ranging from biology and bioinformatics to medicine, physics, mathematics & arts. The broad ambition of his group is to use tools and concepts from network theory to elucidate the complex machinery of interacting molecules that constitutes the basis of (patho-)physiological states. Major areas of interest are network-based approaches to rare diseases, understanding the basic principles of how perturbations of biological systems influence each other and developing novel Virtual Reality (VR) based technologies for analyzing large genomic data.