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Open Lecture: Aim for mechanistic models
24. Februar, 13:00 bis 14:00
Open Lecture by Martin Meier-Schellersheim (National Institute of Allergy and Infectious Diseases NIAID, Bethesda, USA)
Theoretical modeling in cell biology has to strike a balance between finding parsimonious, manageable models with not too many parameters or assumptions that are not directly accessible to experimentation on one hand and, on the other hand, not suggesting models that are regarded as too simplistic and biologically unrealistic. During the search for such balance, it is frequently overlooked that concise models implicitly make many assumptions without actually exposing them to scrutiny. In my talk, I will illustrate this issue using computational models of simple signaling pathways and show how detailed mechanistic models of intracellular reaction networks can be explored efficiently.
Dr. Meier-Schellersheim obtained a master’s degree in physics in 1997 and a Ph.D. in 2001 from the University of Hamburg, Germany. His research focuses on building a bridge between experimental and computational cell biology through the development and application of modeling tools that combine accessible graphical interfaces with the capability to perform spatially and temporally highly resolved simulations, even for models of complex cellular signaling processes.
Interested people from all over the world are invited to join the series of public lectures live broadcasted from the 22. of February to the 4th of March 2021. Designated experts give insights in their research work and visions on future developments. The public lectures are part of the Giersch Summer School & Conference on “Theoretical and Experimental Quantitative Cell Biology” – funded by the Giersch Foundation and FIAS and the scientific networks DynaMem, CMMS, ‘Xidian-FIAS Joint Research Center’ (XF-JRC), the Rhein-Main Universities, and the graduate program GRADE – iQbio.
To follow the lectures please refer to https://fias.institute/en/events/gsic2020, where you can also find information about the event.