Our lab works on living systems and their evolution.

Biological information is encoded in genomes. To produce specific functions, organisms process this information in regulatory and metabolic gene networks. All of these structures evolve, as populations adapt to new environments. 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 natural populations and from laboratory evolution experiments. Our goal is to make evolutionary biology a predictive science.

Our research builds upon physics in two ways. First, biophysics identifies quantitative phenotypes in a cell: molecular binding affinities, gene expression levels, protein folding stabilities, etc. With modern experimental techniques, these phenotypes can be measured in vivo. Second, statistical mechanics provides key concepts to link "microscopic" sequence information and "mesoscopic" phenotypes to "macroscopic" fitness and evolution. In turn, these new applications are likely to shape statistical mechanics over the next decades.

viral evolution	adaptive evolution and dynamical fitness models	evolution of regulation
biological networks	genomic sequences and biomolecules	physics of nonequilibrium and disorder equilibrium statistical mechanics and field theory

Coordinator: Michael Lässig
This DFG-funded Research Center joins biologists and physicists for research in fast-evolving systems: microbial populations in the laboratory, viruses and immune repertoires, and cancer cell populations.