I studied Biochemistry (2004-2008) at the University of Potsdam (Germany). During this time, I also studied two years of Mathematics. I completed my studies in Biochemistry with the diploma thesis on “Identification and characterisation of proteasome-catalyzed spliced peptides” at the University Hospital Charité Berlin. In 2009, I joint the 1+3 years Welcome Trust PhD program at Imperial College London under supervision of Professor Michael PH Stumpf on the topic “Novel descriptive and model based statistical approaches in immunology and signal transduction”. After completing my PhD I was awarded the NC3Rs David Sainsbury research fellowship, continuing my work on understanding signalling processes involved in the innate immune response. During my time at Imperial college I worked on the analysis of life-fluorescent microscopy images describing innate immune cell migration during acute inflammation by developing and applying automated image processing tools. Random walk models and statistical inference approaches were used to elucidate the signalling cascades during wound healing. I furthermore developed Bayesian approaches for model selection and parameter inference as well as Bayesian experimental design with applications in signal transduction and in immunology. After studying the signalling processes of the innate immune response for several years, I became interested again in the adaptive immune response and more specifically, the role of proteasome-catalyzed peptide splicing during antigen presentation. In 2017, I became the head of the research group “Quantitative and Systems Biology” at the Max Planck institute for Biophysical Chemistry in Göttingen (Germany) where, together with my new research team, we continue the research on the proteasome.
The Research Group Quantitative and Systems Biology employs in silico approaches using in vitro and ex vivo as well as in vivo experimental data to study the pathways of the proteasome that regulate the human immune response.
The proteasome is a multicomplex enzyme that catalyzes 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 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-catalyzed hydrolysis and peptide splicing and its importance in the MHC-class I pathway. This includes algorithms to identify spliced peptides from mass spectrometry data and methods to classify and characterize spliced peptides. In the future, this will result in the development of algorithms to predict spliced peptide sequences and their relevance during an immune response.
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 on-going research in systems immunology.