Josefine Asmus

Josefine Asmus

Josefine Asmus

Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
Josefine Asmus
MOSAIC Group
Center of Systems Biology Dresden (CSBD)
Pfotenhauerstr. 108
01307 Dresden
Germany

Phone: +49 351 210-2455
E-Mail: 

Curriculum vitae

Josefine Asmus is a PhD student with the MOSAIC Group since January 2013. She is enrolled in the Biological Systems Path of the center for advancing electronics Dresden (cfaed) Cluster of Excellence of the Technical University of Dresden. She is a German citizen, born in 1986 in Rostock, Germany.


In 2006 Josefine began her studies in Biomathematics at the Ernst-Moritz-Arndt University in Greifswald. In 2009 she did an exchange semester at the Massey University in Palmerston North, New Zealand.


In 2012 Josefine completed her studies with the Diploma Thesis Modelling Transcytosis in Hepatocytes at the Center for Information Services and High Performance Computing (ZIH) at the Technical University of Dresden in the Department for Innovative Methods of Computing (IMC). Her Diploma-project supervisors were Dr. Lutz Brusch and Prof. Dr. Volkmar Liebscher.


After receiving her Diploma in biomathematics, Josefine continued her research at the ZIH/IMC as a research assistant until the end of 2012.


In the MOSAIC Group, Josefine works toward a theoretical understanding of bio-inspired optimization algorithms and the development of an efficient approximate algorithm for design centering and model learning, as inspired by how biological cells process information.

A Word from Josefine...

Describing a system using mathematical concepts is ubiquitous in science and engineering. Models are simplifications of reality and are fundamental to better understanding a system. But models contain parameters that need to be determined with the help of experiments. To understand how cells form tissues, it is necessary to fit experimental data to mathematical models describing this process. Parameter estimation often gives several very different parameters with which the model and the data fit almost equally well. Choosing the parameters that are most robust against small perturbations, i.e. even with small changes in the parameters the model still fits the data, is biologically meaningful. I therefore develop algorithms that efficiently find robust parameters and give information about robustness, such that it is possible to compare and choose between different parameter sets or competing models.