Computational method development for expanding protein-protein interaction networks on the level of isoforms (m/f/d)
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- Veröffentlicht: Donnerstag, 18. April 2024 13:14
Ph.D. or Postdoc Position Computational method development for expanding protein-protein interaction networks on the level of isoforms (m/f/d)
The group Data Science in Systems Biology (School of Life Sciences, Technical University of Munich (TUM)) invites applications for a three-year Ph.D. or postdoctoral position (TV-L E13, 100%) for the development of innovative methods for expanding protein-protein interaction (PPI) networks for isoform-specific interactions in the framework of a collaborative project on refining PPI networks funded by the Klaus Tschira foundation.
Project: While PPI networks are a cornerstone of systems biology research, we and others have reported on challenges and limitations in their use. A potential explanation for this is that PPI networks suffer from study bias as well as a lack of resolution and context-specificity. In a joint project with the Friedrich Alexander University Erlangen and the European Institute of Oncology (IEO, Milan), we seek to address this issue and to expand existing PPI networks from multiple angles. This includes accounting for the immense proteome diversity caused by alternative splicing. Existing PPI networks typically only cover interactions between major isoforms even though we know that isoforms can have different interaction partners. Due to the combinatorial explosion, it is not feasible to test all isoform interactions comprehensively. In the database DIGGER, we partially mitigate this issue by considering information on domain-domain interactions (DDIs), allowing researchers to study the consequences of alternative splicing on the interactome. Using our network-based enrichment tool NEASE, we can further show that this offers valuable insights when evaluating transcriptome data. However, the information on DDIs is scarce, and prediction algorithms are outdated and unavailable. Furthermore, existing methods do not use recent advances in deep learning that help in modeling and understanding PPIs [6]. The successful candidate will re-assess previous and develop new methods for DDI prediction and integrate them into the DIGGER https://exbio.wzw.tum.de/digger/ and HIPPIE databases https://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/
More information can be found here: https://www.mls.ls.tum.de/daisybio/aktuelles/nachricht-detail/article/open-phd-postdoc-position-on-refining-protein-protein-interaction-networks-1/
