Berg group

We work on inverse statistical problems and stochastic systems in order to understand information processing in biological systems and biological evolution. Recent applications are in the fields of drug resistance to cancer therapy, where we do both theoretical modelling and cell line experiments, the inference of signalling networks, and stochastic dynamics in gene regulation.

Bollenbach group

How do cells respond to combinations of drugs and other signals? Can the evolution of antibiotic resistance be predicted and prevented? How do microbial ecosystems function? To tackle questions like these and ultimately discover the general principles and laws of living systems, we combine concepts and techniques from physics with molecular biology and advanced high-throughput experiments.

Gompper group

We develop theoretical and numerical models to understand physical mechanisms governing the structure and dynamics of soft, active, and living matter. Research topics include the interactions of proteins and nanoparticles with membranes, the behaviour of blood cells under flow, the non-equilibrium dynamics of the cytoskeleton, the motion and flow generation of microswimmers (bacteria, sperm, cilia, artificial self-propelled particles), and the growth of tissues. Our overarching goal is to elucidate the physical principles of non-equilibrium -- active and driven — systems.

Krug group

We develop population genetic models to describe and analyse how populations evolve to adapt to a new environment. A key goal is to understand the factors that shape the pathways along which evolution can proceed and thus ultimately determine the predictability of the evolutionary process. Applications derive mostly from the field of experimental evolution, in particular the evolution of drug resistance in microbes.

Lässig group

Our lab works on living systems and their evolution. How does this dynamics work? Modern molecular biological data provide unprecedented opportunities to understand evolution in a quantitative, empirical way. Conversely, evolutionary analysis becomes a tool to understand biological function. We work with data from fast-evolving natural populations and from laboratory evolution experiments, including the influenza virus, E. coli, and yeast. Our goal is to make evolutionary biology a predictive science.

Maier group

How do bacteria control molecular forces to shape multicellular communities called biofilms? Can we correlate biofilm structure with tolerance against drug treatment? These questions are at the center of research at the single cell level. At the population level,  we design methods for quantifying costs and benefits of horizontal gene transfer between bacteria. In our lab, physicists and biologists work in close collaboration using tools from nanotechnology, image analysis, molecular biology, and experimental evolution.