Prof. Niko Beerenwinkel

Institution: ETHZ, Zürich, D-BSSE – Computational Biology Group (CBG)

Link to lab page:

Field of Research:



We are active in the fields of computational biology, bioinformatics, biostatistics, and systems biology. Our activities include the development of mathematical and statistical models, their efficient implementation in computer programs, and application to biomedical problems. We are interested in disease-associated biological networks and in pathogen evolution associated with disease progression. Our ultimate goal is to predict the effect and to support the design of medical interventions in complex, rapidly evolving, biological systems, such as virus populations, bacterial colonies, and tumors. We make use of high-throughput molecular profiling data and apply high-dimensional statistical modeling in order to study intracellular as well as intercellular biological networks and to predict the effect of genetic alterations. We model the evolutionary dynamics of pathogen populations to investigate virulence, disease progression, immune escape, and drug resistance development. Our models aim at understanding evolutionary escape and improving diagnostics, prognostics, and rational treatment design. Niko


Specific Contribution to research in TargetInfectX:

In this project, we aim to understand the signaling cascades that are triggered in host cells upon infection. Our goal is to construct probabilistic models of the cellular network responsible for pathogen sensing and inflammation and to identify host proteins as new potential drug targets.
We analyse the RNAi infection screens performed by different groups involved in InfectX. These intervention experiments shed light on the set of host proteins involved in the signaling pathway and on epistatic interactions among them. Thus, RNAi screens offer indirect evidence of the structure of the underlying signaling network. We develop statistical methods for the analysis of RNAi data. This includes correcting off-target effects, ranking of genes to identify primary hits, reconstruction of signaling pathways, comparison of entry mechanisms for different pathogens, and analysis of single-cell imaging data.



Lab Members involved in TargetInfectX:

Martin Pirkl, PostDoctoral Fellow


Simon Dirmeier, PhD Student


Sumana, Srivatsa, PhD Student

Pathway reconstruction has proven to be an indispensable tool for analyzing the underlying molecular mechanisms of a cell. There are existing probabilistic graphical models, which have been designed to reconstruct pathways from high dimensional observations resulting from RNA interference (RNAi) experiments. These models assume that the short-interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, in reality most siRNAs exhibit strong off-target effects, which further confound the RNAi screen data, thus resulting in unreliable reconstruction of networks. Sumana is trying to use this combinatorial knockdown data as a result of siRNA off-target effects to accurately infer the underlying pathway