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 [1]. A potential explanation for this is that PPI networks suffer from study bias as well as a lack of resolution and context-specificity [2]. 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 [3]. Due to the combinatorial explosion, it is not feasible to test all isoform interactions comprehensively. In the database DIGGER [4], 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 [5], 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 However, the information on DDIs is scarce, and prediction algorithms are outdated and unavailable.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 (exbio.wzw.tum.de/digger/) and HIPPIE databases (cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/).
Environment: TUM is one of Germany's most highly-ranked academic institutions. Within TUM, the candidate will be embedded in the group Data Science in Systems Biology (daisybio.de) of Prof. Markus List located at the School of Life Sciences campus in Freising. The candidate will benefit from a stimulating research environment covering many aspects of systems biology, from studying gene-regulatory mechanisms to drug repurposing. Furthermore, the candidate will work closely with the other Ph.D. students and Postdocs in Erlangen and Milan in a highly collaborative fashion, including regular Zoom meetings and the possibility of an extended research stay. The candidate will have access to state-of-the-art computing facilities for bioinformatics data processing and machine learning.
Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. Regarding personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung / (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.
Equal opportunity: TUM is an equal opportunity employer. As such, applications from women are explicitly encouraged. Preference will be given to candidates with disabilities who have essentially the same qualifications.
References:
1. Lazareva O, Baumbach J, List M, Blumenthal DB. On the limits of active module identification. Brief Bioinform. 2021. doi:10.1093/bib/bbab066
2. Lucchetta M, List M, Blumenthal DB, Schaefer MH. Emergence of power-law distributions in protein-protein interaction networks through study bias. bioRxiv. 2023. doi:10.1101/2023.03.17.533165
3. Yang X, Coulombe-Huntington J, Kang S, Sheynkman GM, Hao T, Richardson A, et al. Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing. Cell. 2016;164: 805–817. doi:10.1016/j.cell.2016.01.029
4. Louadi Z, Yuan K, Gress A, Tsoy O, Kalinina OV, Baumbach J, et al. DIGGER: exploring the functional role of alternative splicing in protein interactions. Nucleic Acids Res. 2020. doi:10.1093/nar/gkaa768
5. Louadi Z, Elkjaer ML, Klug M, Lio CT, Fenn A, Illes Z, et al. Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases. Genome Biol. 2021;22: 1–22. doi:10.1186/s13059-021-02538-1
6. Evans R, O’Neill M, Pritzel A, Antropova N, Senior A, Green T, et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv. 2021. p. 2021.10.04.463034. doi:10.1101/2021.10.04.463034
Requirements: We are seeking outstanding candidates holding a master’s degree in computer science, computational biology, bioinformatics, or similar, with strong analytical and problem-solving skills, who are strong in written and oral communication (in English), and who have experience in bioinformatics method development and analysis of PPI networks. Extensive knowledge of and practical skills in statistical analysis, data mining, data integration, machine learning, and programming in R or Python are also required.
Application process: Applicants should send a dossier containing a motivational statement (max. one page), a curriculum vitae summarizing qualifications and experience, a list of publications, degree certificates, names, and the email addresses of at least one referee as a single PDF document via e-mail to Markus List (markus.list@tum.de). Online interviews will be conducted with selected candidates, and the remaining shortlisted candidates may be invited to the TUM campus in Freising, Germany, for face-to-face meetings with the group. The deadline for applications is 2 May 2024. The intended project start is 1 July 2024.