International Max Planck Research School for Genome Science

Liepe, Juliane, Dr.

Research Group Leader at the Department of Molecular Biology at the Max Planck Institute for Multidisciplinary Sciences


  • Since 2025: Head of Core Facility for Data Sciences and Biostatistics at the Max-Planck Institute for Multidisciplinary Sciences (Göttingen, Germany)
  • Since 2017: Research Group Leader Quantitative and Systems Biology at the Max-Planck Institute for Multidisciplinary Sciencesy (Göttingen, Germany)
  • 2013 - 2016: NC3Rs David Sainsbury Research Fellow at Imperial College London (UK)
  • 2013: Postdoc in the group of Theoretical Systems Biology at Imperial College London (UK)
  • 2009 - 2013: PhD in the group of Theoretical Systems Biology at Imperial College London (UK).
  • 2008 - 2009: M.Sc. in Bioinformatics and Theoretical Systems Biology at Imperial College London (UK)
  • 2006 - 2008: Studies of Mathematics at University of Potsdam (Germany)
  • 2004 - 2008: Diploma in Biochemistry at University of Potsdam (Germany)



Major Research Interests
The Quantitative and Systems Biology group employs in silico approaches, using in vitro and ex vivo and in vivo experimental data, to study the pathways of the proteasome that regulate the human immune response.
The proteasome is a multicomplex enzyme that catalyses protein degradation. Apart from its function in protein metabolism, the proteasome regulates the immune system through antigen presentation, where the proteasome produces most of the epitopes presented in the MHC-class I pathway. These epitopes can be generated by simple cut, or cut-and-paste events. Latter so-called proteasome-generated spliced peptides (PSPs) represent more than one third of all epitopes bound to MHC-class I molecules.
We have developed a set of mathematical and bioinformatics tools to study the details of proteasome-catalysed hydrolysis and peptide splicing and its importance in the MHC-class I pathway. This includes algorithms to identify PSPs from ex cellulo mass spectrometry data and methods to classify and characterise PSPs. In the future this will result in the development of algorithms to predict PSP sequences.
Our group therefore focuses on the development and application of diverse approaches, ranging from efficient mass spectrometry search algorithms and machine learning algorithms to dynamical modelling and model calibration approaches.
The proteasome already is a target for therapeutic trails against cancer and infectious diseases, but its full potential still needs to be explored. This research and the in silico tools developed here will aid such translational aspects and advance the ongoing research in systems immunology.

Homepage Department/Research Group
https://www.mpinat.mpg.de/de/liepe

ORCID: 0000-0003-2515-9707