The common theme of my research is the application of methods from Statistical Physics to solve problems of biological relevance. All my projects are in close collaborations with experimental groups often involving interdisciplinary teams of researchers. Given the complexity of biological systems all projects include the use of computation at some level but the focus of the research is the development of quantitative computable models and not so much the computation in and of itself.

One focus area is the detailed modeling of biophysical properties of nucleic acid molecules. The nucleic acid molecules DNA and RNA are fundamental players in all living cells. A lot can be learned about their function and their interactions with proteins or other nucleic acid molecules through biophysical experiments such as fluorescence tagging, Förster Resonance Energy Transfer (FRET), force-extension measurements using optical or magnetic tweezers or AFM, or nanopore experiments. In all these experiments, the experimental observables (e.g., fluorescence intensities, transfer efficiencies, force-extension curves, or translocation time distributions) are only rather indirect probes of the real quantities of interest, namely the microscopic mechanisms underlying a specific biological phenomenon. Thus, these experiments require theoretical models that link microscopic mechanisms to biophysical observables and thus allow extraction of mechanistic insight from measurement of biophysical observables. The development of these models is at the heart of our research. Recent examples of this work include a model for the interplay between RNA secondary structure and single-stranded binding proteins, and models for dynamic DNA origami devices.

The other focus area is the analysis of biological sequences. Biological sequences are produced in an astonishing volume, which grows exponentially with a rate faster than Moore's law (the rate at which computing speed doubles). Over the last decade, high throughput sequencing technologies have revolutionized the way biological systems can be interrogated putting the power to sequence the equivalent of entire human genomes into the hands of individual researchers. Methods from Statistical Physics are exquisitely suited to extract biological information from these large quantitative data sets. My lab is involved in many collaborations that rely on advanced analysis of such high throughput sequencing data. With Kurt Fredrick in Microbiology, we are dissecting the process of prokaryotic translation using ribosome profiling, a method that allow taking a snapshot of the positions of all ribosomes in a cell simultaneously. With Guramrit Singh, we are studying the role of different components of the exon junction complex, a protein complex that is placed on a messenger RNA to mark splice junctions and is important in quality control of the transcriptome. In each of these collaborations we simultaneously address important biological problems and aim to improve computational analysis methods that can be applied to related biological problems.

Research in the Bundschuh lab is supported by the National Science Foundation.