Physics of Complex Biosystems,
Physik-Department, Technische Universität München,
James-Franck-Str. 1, 85748 Garching, Germany

How to reach us

Postal address

James-Franck-Str. 1,
85748 Garching


Name Tel. Fax Email Raum
Daniela Neufang +49 (89) 289 - 12662 +49 (89) 289 - 12638 Room  3229
Claudia Öckler +49 (89) 289 - 12662 +49 (89) 289 - 12638 Room  3229

How to find us

Take U6 to "Garching - Forschungszentrum". Leave the station in the direction of travel. 

 After ca. 100 m (after having passed the "Institute for advanced study" with the waterfall) take a right into James-Franck-Strasse, where you see a white sign "TUM physik department". We are on the first floor in the corridor opposite the "Bibliothek". 

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Open Positions

Bachelor, Master and PhD Thesis

We welcome applications from both experimentally and theoretically minded people. Please contact us directly if you are interested in doing a thesis in our group.

Bachelor Thesis Offers

Pattern Formation via Local Cell-Cell Signaling with Variable Interaction Ranges

Pattern formation phenomena occur in many different contexts ranging from physical and chemical systems to developmental biology and synthetic biology. In developmental biology, the correct relative positioning and determination of different cell fates is essential to ensure that the organism functions correctly. Whilst many models for pattern formation exist, the focus here is on cellularized systems with interactions only between nearby cells. This form of communication has previously received less attention from theoretical research than e.g. long-range diffusible signals as a means of communication. Our goal is to explore fundamental limits, patterning concepts, and engineering potential with a simple top-down model. Building on previous work, the project will concentrate on searching mechanisms of pattern formation with the goal of investigating how the interaction range can influence the ability to realiably create paradigmic patterns like striped "French Flag" patterns with tunable widths of the stripes. Prior knowledge in biology/biophysics is not needed, but interest in theoretical physics and computational problem solving is expected.

Master Thesis Offers

Simulating the collective motion of encapsulated enzymes in external substrate gradients

Recent experiments revealed that the diffusion coefficients of enzymes can depend on the concentration of their corresponding substrates: the enzyme diffuses faster in the presence of more substrate. The aim of this thesis is to understand the consequences of this effect on the collective motion of enzymes. Imagine a vesicle immersed in a substrate gradient and loaded with thousands of units of a specific enzyme. What would happen to this vesicle? Would the non-uniform motion of the enzymes inside affect the shape of the vesicle? And what kind of deformations could be produced? Would it be possible to cause the movement of the vesicle along a preferred direction? To tackle these questions, you will be involved in the implementation of Brownian dynamics simulations combining a mesh description of the vesicle, the diffusion-dependent movement of enzymes and the interactions between the enzymes and the vesicle. With this work you can contribute to some of the latest developments in enzyme dynamics and active matter. Moreover, your results can be utilized to design and analyze experiments. Prerequisites: Interest in biophysical systems and in simulations of dynamical systems.

Studying the bacterial life cycle and morphology using flow cytometry

The flow cytometer is an optical device that can measure cell properties at a single cell level with a high throughput. The aim of this Master thesis is to explore the dynamics of bacterial populations using flow cytometry to address the following questions: How does a cell population change at the onset of growth arrest? What are the dynamics of cellular viability during starvation? Do bacteria uniformly die under starvation or are there growing subpopulations? Can one accurately infer morphological information from flow cytometry data? How can cell dynamics under starvation be modeled with the help of stochastic processes?