| May 19-23, 2023 | Optical tweezers and microrheology, Prof. Timo Betz. The assessment of the mechanical properties of the cytoskeleton is an integral step for the quantification of the active cytoskeleton. In this course, we will introduce the physical basis for optical tweezers and for frequency-dependent rheology of complex material and the cytosol. In the hands-on course, you will have the possibility to do the final steps in the alignment and setup of an optical tweezer system. In the second part, you will use active microrheology to measure the mechanical properties of a hydrogel that shares mechanical properties with typical cellular cytoplasm. |
| Aug 23-25, 2023 | Atomic force microscopy, Prof. Andreas Janshoff. This course will cover the basics of AFM imaging and force curve acquisition. We will discuss how to chose the right cantilever and explore the limits of resolution. IN hands-on examples we will learn how to work up the data to obtain viscoelastic parameters from soft samples such as living cells. |
| Nov 16-17, 2023 | Microfluidics, Prof. Sarah Köster. In this course, you will learn how to fabricate your own microfluidics devices for microscopy experiments. We will then conduct laminar flow experiments and produce water-in-oil emulsions. |
| Dec 1/8, 2023 | Cytosim Hackathon, Dr. Yoav Pollack. This hackathon is envisioned also as an opportunity for experiment-oriented students to learn how to use simulation resources using an intuitive software framework. (computational enthusiasts are also welcome and encouraged). In this Hackathon, we will work together to build minimal 'Proof of Concept' simulations using the Cytosim package that is designed specifically for simulating cytoskeletal elements. After a quick intro to the basics of Molecular Dynamics (MD) simulations and to Cytosim, we will start planning together (and trying out) simulations that could teach us something about the real-world experimental systems. On the 2nd day of this hackathon, we will move on from the initial exploratory phase to focus more on producing a presentable Proof of Concept simulation and analysis. |
| April 13, 2024 | X-ray imaging, Prof. Tim Salditt. The course will offer an opportunity to participate in a synchrotron experiment at DESY. Students will learn basic knowledge about running an X-ray tomography experiment on biological tissues on cells with online data analysis. |
| May 2-3, 2024 | Protein Biochemistry, Prof. Claudia Steinem and Dr. Dominik Ruppelt In the course, you learn how proteins are expressed, purified, and biochemically analyzed. |
| May 15-16, 2024 | Modelling approaches to cytoskeletal rheology, Prof. Peter Sollich. The course will review central notions in mechanical behaviour and rheology, including viscoelasticity, dynamic moduli and nonlinear response. You will then learn about, and explore in computational experiments, key modelling approaches to cytoskeletal rheology that exploit concepts from polymer physics and active matter. |
| May 24, 2024 | Excursion to Bayer Pharmaceuticals, Berlin, Dr. Florian Mann |
| Jun 12-13, 2024 | Python based image analysis using skimage and napari, Dr. Antonio Politi. In this course, you will be introduced to essential concepts of image analysis. You will learn how to use python for image analysis and take advantage of the large and growing number of specialized python libraries. Learning python is useful in general because it is currently the most popular language for scientific computing and data science. In addition, since a few years a python based image visualisation tool, namely napari, exists that allows powerful and flexible n-dimensional image data visualisation, including overlay of segmentation and annotation layers. |
| Jun 18-19, 2024 | Cell Biology for Physicists, Prof. Melina Schuh. In this course you will learn basic mammalian cell culture techniques and apply the trim-away technique to clear cells of endogenous proteins. Participants will use confocal microscopy on live cells as well as SDS Page and Western blot to analyze protein expression and localization. We will also cover the principles of image analysis. MOVED to 2025 |
| Oct 15, 2024 | Excursion to Zeiss, Jena |
| Nov 6-7, 2024 | Molecular dynamics simulations, Prof. Dr. Helmut Grubmüller and Dr. Maxim Igaev. Molecular dynamics simulations are a method for describing the dynamics of macromolecules such as proteins, lipids, or synthetic polymers. The movements of all the individual atoms of a system are calculated here by computer-based numerical integration. The aim of this course is to familiarize you with the basics of this method, and to learn how to perform and analyse molecular dynamics simulations in practice. For this purpose, simulations of a small protein model system are carried out. |
| Nov 14-15, 2024 | Stochastic simulation techniques, Prof. Stefan Klumpp. In this course, you will learn about simulation techniques that can be used to study cytoskeleton-related questions computationally. The course will consist of an introductory overview lecture and hands-on excersises. In the introduction, we will present general methods as well as available simulation tools. In the hands-on part you will work on concrete practical problems and run some simulations yourself using available computational tools (probably in groups of two students, but we will make the dependent on your backgrounds). In addition we will discuss data analysis and visualisation. The course will be concluded by short student presentation of the results obtained. Students with and without programming experience are welcome, please let us know what experience you have. |
| Dec 3, 4 and 6, 2024 | Inverse Problems, Prof. Anne Wald. Inverse problems are an important class of mathematical problems with applications in the natural sciences. Whenever a quantity cannot be measured directly, we have to evaluate indirect measurements, i.e., we draw conclusions on its properties from its influence on some measurable quantity. In many cases, a direct inversion of the relation between desired quantity and measurement data leads to useless results, since noise in the data can be strongly amplified. In this course, we introduce the basic mathematical concepts and discuss regularization methods, which can provide stable solutions with respect to noise. |
