DNA nanotubes are widely used as a mimic for cytoskeletal filaments in bottom-up synthetic biology. Using a synthetic starPEG construct that acts as a crosslinker, we succeed in bundling the few nanometer thick DNA nanotubes. In bulk they self-assemble into micron-scale rings. We achieve their contraction upon temperature increase or molecular depletion with crowing molecules such as dextran (in collaboration with Kierfeld group, TU Dortmund).

Galaxies are embedded in a rich and complex atmosphere – the circumgalactic medium (CGM). Understanding the processes going on in the CGM is inevitable for a self-consistent model for galaxy evolution. Thus, we will shed some light on open questions about galaxy clusters using numerical simulations. We will analyze the already existing cosmological state-of-the art simulation IllustrisTNG and also write new types of simulation.

Most animals exhibit bilateral symmetry, but asymmetric traits have repeatedly evolved in different taxonomic groups. However, the genetic mechanisms responsible for asymmetric trait variation remain unclear. We will use the scale-eating fish, Perissodus microlepis, to dissect the genetic basis of its remarkable morphological and behavioural asymmetry. This study will yield important insights into the mechanistic underpinnings of asymmetric development and the origin of evolutionary novelty.

Highly emotional memories are processed differently from neutral ones. For negative experiences, this can result in maladaptive memory formation which may foster emotional psychological disorders. This project aims to improve our understanding of adaptive and maladaptive memory processing. We will analyze brain activity in tasks that model maladaptive memory symptoms. By this, we hope to identify entry points for treatments that counteract maladaptive memory formation.

Our study aims to analyse and map the cytoarchitecture of the human hypothalamus in histological sections of 10 postmortem brains. As a result, we want to develop a high-resolution 3D reconstructed histological model of the hypothalamus and its nuclei as a tool for assessing the structure-function relationship and a probabilistic cytoarchitectonic map of the hypothalamus that will reflect the variability of hypothalamic nuclei between individual brains, in terms of size and location in standard reference space.

The ability of non-resistant bacterial pathogens to survive antibiotics during infection (tolerance) contributes not only to global rise of antibiotic resistance, but also to chronical relapse of infections. The aim of the project is to understand what contributes to bacterial revival after antibiotic treatment and the underlying biological pathways. The findings of this project will contribute to better informed decisions on the selection of antibiotics to treat infections and prevent relapse.

Viruses are the most abundant biological entities on Earth. Although they infect members of the three domains of life, little is known about the infection mechanisms of archaeal viruses. The aim of this research is to gain insight into the interaction between halophilic archaeal cells and their viruses by using a combination of light and electron microscopy with molecular biology and virological techniques.

Carbon nanotube (CNT) resonators will be designed and fabricated to exploit their sensing properties. We will graft a single-molecule magnet (SMM) on such a CNT resonator in order to manipulate its spin states via the mechanical motion of the CNT. Using this nanomechanical approach, single-molecule magnets will be investigated with the long-term prospect of applying them in future quantum technologies.

Due to technological advances we can now build devices that actively manipulate quantum mechanical objects, and using quantum objects as information carriers has many important implications for future communication. Quantum information can be used to achieve perfect security and provide efficiency for communication networks. This research project focuses on designing secure and anonymous quantum communication protocols in an effort to build a future quantum internet.

The detection of gravitational waves (GWs) has opened a new window on the universe, through which we can study the physics of black-hole and neutron-star mergers. By analyzing GWs we can infer properties of the corresponding astrophysical systems. Current analysis methods are however too computationally expensive to deal with the growing amount of data. My research is thus concerned with the development of more efficient methods for the GW analysis using powerful machine learning methods.