Modeling and Simulation of Biological Processes
Research agenda
Biomedical Data Science and Systems Modeling?
The lab's research aims to pioneer the integration of quantitative systems models with AI/machine learning approaches to uncover novel therapeutic strategies for cancer treatment. With a focus on deep learning techniques, we seek to seamlessly integrate large-scale datasets with patient outcome data, thereby elucidating intricate tumor dynamics and therapeutic responses.?
- Leverage innovative technologies like single-cell sequencing and deep learning
- Unravel the complexities of cancer biology and identify clinically relevant insights
We foster interdisciplinary collaboration through open communication and talent development, ensuring alignment among scientists across various fields. With a commitment to effective project management, our ultimate goal is to translate cutting-edge research findings into measurable improvements in patient outcomes, thereby advancing the forefront of cancer therapeutics.
Research topics
Investigate patient outcome based on large-scale Omics datasets
- Deconvolute?the complex interactions in the tumor microenvironment
- Characterize?the?tumor antigen and immune environments, both locally and systemically
- Predict outcome of treatments?(e.g., with SRT, RLT, IO agents)
- Analyze patient?journeys to support personalized medicine approaches
- Study biological mechanisms?and?processes of therapeutic?relevance?(e.g., signaling and?transmembrane?proteins)
- Develop novel approaches for mechanistic analysis using?MassSpec?data
- Enable learning across diverse data set by integrating different data modalities and modeling approaches
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Does any of those areas interest you?
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We have various topics for B.Sc., M.Sc. or Ph.D. thesis.
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Lectures & Seminars
Contact details
Chair of Modeling and Simulation of Biological Processes
Building A, 2nd floor, A002
Gutenbergstra?e 7
86356 Neus??
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