Identifying host dependency factors of viruses for therapeutic treatment by a bioinformatics approach

Start: SS 2022 (still open!)

Background and Goals: With a death toll of over 120 million and increasing case numbers and emerging variants, the corona pandemic remains a public health emergency of international concern. Treatment options are largely supportive, but not specific. The application of virus directed treatments is limited as viruses adapt to changing environmental conditions, particularly when they are under selection pressure. Therefore, employing host-directed antiviral therapy to disrupt the virus life cycle is of great importance.

We are analyzing the life cycle of several infection causing pathogens inside host cells and particularly corona viruses comprising entry, replication and release of the pathogen. Together with partners in Frankfurt and Heidelberg, we are performing gene knockdown screens on corona viruses infected human host cells. Together with a range of omics, genomics and other data, we aim to identify host dependency factors. These are proteins in the human host cell which are essential for the pathogens, but are not for the cell. For these, we want to identify drugs efficently inhibiting such proteins leading to antiviral and antibiotic therapeutics.

Our high level goals are to

• identify druggable host dependency factors from multi-omics data, and
• study the cellular pathomechanisms at the molecular level based on multi-level profiling facilitated by network analysis.

Outcomes from our analyses will be implemented and validated in vitro using pandemic pathogens, including influenza and coronaviruses as infectious model systems in Frankfurt. Drug screenings and multi-scale microscopic imaging will be performed in Heidelberg

Methods: We analyse data using basic statistics and machine learning. For this, the R programming environment is used (www.r-project.org)

Application requirements: You should have a completed Bachelor's degree in molecular medicine, biology, virology, biotechnology, bioinformatics (or an equivalent subject). A basic knowledge of cell biology is required. Basic programming knowledge and skills would be good but ars not necessary, you will learn this during your thesis. Similarly, a machine learning or modeling background would be good, but is not necessary. In turn, you should have a lively interest in systematically detecting functional patterns in high dimensional data and gene networks.

Contact: Send your complete application documents (letter of motivation, references, CV incl. practical experience) by e-mail to