Computational Regulatory Genomics

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Computational Regulatory Genomics

We develop and use computational and genomics approaches to understand the biology of gene regulation in eukaryotic organisms.

The expression of genes is a multi-step process that is tightly controlled on several levels — a large number of protein and RNA factors and DNA and RNA sequence elements enable the precise regulation of interacting gene products. According to our current understanding, this complexity in higher organisms is not achieved by a more complex repertoire of parts, i. e. genes, but instead by the more complex regulation of the parts. It is a key challenge to decipher these complex networks of players and interactions, and to move biology from case studies to an integrated, global approach.

Computational biology has become indispensable to analyze and ultimately make sense of the current large-scale data sets that look at the phenomenon of gene regulation from different angles.  Our long term goal is to investigate how regulatory networks enable the correct development of complex organisms, with their multitude of cell types that carry out different functions despite the same genome. To this end, we are developing computational methods that use diverse sources of molecular information; we frequently frame questions as classification problems and use probabilistic modeling approaches to make testable predictions.

To generate genome-wide quantitative data and/or to validate predictions, we have also set up a (smaller) genomics wet lab. Projects are often done in close collaborations that integrate experimental and computational approaches, on animal and plant model systems such as Drosophila and Arabidopsis as well as on human data.

The lab has a dozen or so long-term members (staff, PhD students, and postdoctoral researchers) and additional undergraduate or Master’s students. With its interdisciplinary focus, the lab members’ backgrounds range from Biology, Biomedical Engineering, Electrical and Computer Engineering, Computer Science, Applied Math, to Physics.