Research
Publication
Research Projects
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Radiation-induced immune responses following targeted radiotherapy
Radioimmunotherapy (RIT) is an innovative approach to cancer treatment that combines the targeted delivery of radiotherapy with the precision of immunotherapy. This project aims to model immune responses after RIT using mechanistic ODE-based models. The results will help to optimize preclinical experiment designs and identify biomarkers and response targets.
Methods: QSP modeling, Multi-scale modeling
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Reciprocal interactions between the tumor microenvironment and the lesional microbiome
Mycosis fungoides (MF), the most prevalent cutaneous T-cell lymphoma, is characterized by malignant T-cell proliferation within a chronic inflammatory environment with variable disease progression from indolent to aggressive forms. Our hypothesis is that the interaction between malignant and reactive T cells in MF lesions is influenced by the microbiome, thereby affecting disease progression. Our research aims to link these interactions with the microbiome to identify prognostic markers and develop novel therapies.
Methods: Immune repertoire analysis, Microbial read detection, Microbial functional analysis, Cell-Cell communication.
Funded by: DFG
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Systemic immune responses following radiotherapy
Experimental evidence demonstrates immunological interactions between the lung and gut microbiome (gut-lung axis) that may influence host responses after radiotherapy. This project investigates the influence of gut microbiome dysbiosis on lung inflammation and immunity by developing a quantitative systems pharmacology (QSP) model. It aims to understand how microbial cytokines and metabolites affect lung tissue and to identify predictive biomarkers and treatment strategies.
Methods: QSP modeling, Virtual population generation
Funded by: DAAD - Brigitte and Dr. Konstanze Wegener Foundation - GRK 2762
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Eco-evolutionary processes that underlie diverse and heterogeneous systems
Quantifying the relative importance of neutral processes versus selection in shaping biological communities is critical in evolutionary biology and ecology. By analyzing different data sets, we've shown a link between the structure of species abundance and patterns of diversity. Our computational models suggest that deterministic selection forces may be responsible. This project aims to uncover the cause of this association and the conditions under which it can be observed in a population.
Methods: Population genetics models, Agent-based modeling, Machine learning
Funded by: DFG