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Biological systems belong to the most fascinating and mysterious entities to be found in our universe, and yet their functioning ultimately rests entirely on very well known laws of nature. In order to understand how biological systems work we do not need to discover previously unknown bits of physics and chemistry; the task is rather to piece the puzzle together and learn how simple laws can explain complex phenomena. Biological Physics is thus a synthetic science, much like Statistical Physics, from whose arsenal of methods it frequently and heavily borrows.

In my research I look at various exciting problems in Biological Physics, dealing, among other things, with lipid membranes, proteins, viruses, or DNA. In all cases I am most interested in phenomena that occur on length scales larger than atomic resolution (i.e., I'm not looking at the specific chemistry) but smaller than whole cells. On these scales many fundamental physical concepts have a big impact on biology, among them thermal fluctuations, cooperativity, self-assembly, or elasticity. For instance, due to their surfactant-like nature individual lipid molecules in an aqueous environment spontaneously aggregate into membranes, which span laterally over scales many orders of magnitude larger than their thickness. These quasi-two-dimensional fluid surfaces resist bending, a continuum elastic concept, but since the associated moduli are only about one order of magnitude bigger than thermal energy, membranes can exhibit large thermal undulations that can very substantially affect their behavior.

In my research I use both theoretical and computational techniques. On the theoretical side I use tools from continuum elastic descriptions, differential geometry, density functional theories, and statistical thermodynamics. On the computational side I mostly use what is known as "coarse-grained simulations". This means that the physical system is not represented on the computer in atomic detail. Rather, a much smaller number of degrees of freedom is used to describe a lipid or a protein. Giving up chemical resolution implies that questions dependent on it cannot be addressed. However, on sufficiently large length scales such detail hardly matters, and the physical properties relevant at this scale, for instance the bending rigidity of a membrane, can be accounted for very appropriately.

The benefits to be reaped from these simplified theoretical and computational descriptions are twofold: First, one can study much larger systems on much longer time scales with much better statistics, and thus access a new arena for physical questions. Second, if a highly simplified model still manages to capture key aspects of the situation at hand, chances are that we have identified the key bits of physics that matter. In other words, we might just have understood something deep about our system that would not be immediately obvious when looking at the original hugely more complicated situation.