Josefine Asmus was a PhD student with the MOSAIC Group from January 2013 until May 2017. She did her PhD in the Biological Systems Path of the center for advancing electronics Dresden (cfaed) Cluster of Excellence of the Dresden University of Technology. She is a German citizen, born in 1986 in Rostock, Germany. After leaving the MOSAIC Group, Josefine became a researcher at the Max Planck Institute for the Physics of Complex Systems.
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 developed a theoretical understanding of bio-inspired optimization algorithms, leading to the discovery and study
of an efficient randomized algorithm for approximate design centering and robustness estimation, as inspired by how biological cells process information. This is the first time that this NP-hard problem is efficiently approximated in high dimensions.
J. Asmus, An efficient randomized approximation algorithm for volume estimation and design centering. PhD thesis, MOSAIC Group, Faculty of Computer Science, TU Dresden, 2017. (PDF)
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.