Dr. Sarah Brüningk
Dr. Sarah Brüningk
Lecturer at the Department of Biosystems Science and Engineering
Additional information
Research area
My research focuses on the interface of biology, medicine, mathematical modeling, and data science to provide personalized predictions for healthcare applications based on a variety of input data types. I have a particular interest in data-driven models for rare conditions where data is limited but the impact a prediction model could make in clinical application is particularly high. Specifically, I investigate personalized therapy regimes for pediatric brain tumor patients, including low and high-grade glioma with an emphasis on pediatric diffuse midline glioma. By using tailored input data types and a combination with mechanistic mathematical modeling I devise prediction pipelines that aim for an application as in silicon trial simulation platforms. In this context I investigate generative AI models for anatomical predictions of tumor growth on magnetic resonance imaging, radiotherapy response prediction based on imaging and multi-omics inputs, as well as biomarker identification from histology and magnetic resonance data. Apart from pediatric oncology, I am also involved in data-driven predictions to describe recovery from traumatic spinal cord injury and to detect critical conditions in pediatric intensive care units, such as sepsis.
I am a physicist by training and obtained a BSc. in Physics and MSc. in Physics (Biophysics) from the Technische Universität München (Munich, Germany) in 2011 and 2014 respectively. After working for a few months as a medical physicist in the RadiationOncology Department at the Klinikum rechts der Isar (Technische Universität München University Hospital), I moved to the Institute of Cancer Research (London, UK) to pursue a PhD. Here, I obtained a PhD in Biophysics in 2019 on the topic of "Analysis and simulation of combination treatments of radiation and focused ultrasound mediated heating". Following a short stay at the ICR as a postdoctoral research fellow I moved to ETH Zurich (Switzerland) in September 2019 where I joined the Computational Biology lab of Prof. M. Claassen at the Institute of Molecular Systems Biology. In April 2020 I transferred to the Machine Learning and Computational Biology Lab (MLCB) of Prof. Karsten Borgwards at the Department of Biosystems Science and Egineering). Here, I started my Basel Research Centre for Child Health Postdoctoral Excellence Fellowship in October 2021. Due to the move of the MLCB lab, I transferred in February 2023 to the Biomedical Data Science lab (BMDS) of Prof. Catherine Jutzeler at the Department of Health Sciences and Technology. Since August 2023, I am leading the "Pediatric Oncology Application" subgroup with this lab.
CV PDFCourse Catalogue
Autumn Semester 2024
| Number | Unit |
|---|---|
| 376-1725-00L | Introduction to Python Programming |
MLCB publications
Determinants of SARS-CoV-2 transmission to guide vaccination strategy in a city
Sarah C. Brüningk*, Juliane Klatt*, Madlen Stange*, Alfredo Mari, Myrta Brunner, Tim-Christoph Roloff, Helena M. B. Seth-Smith, Michael Schweitzer, Karoline Leuzinger, Kirstine K. Søgaard, Diana Albertos Torres, Alexander Gensch, Ann-Kathrin Schlotterbeck, Christian H. Nickel, Nicole Ritz, Ulrich Heininger, Julia Bielicki, Katharina Rentsch, Simon Fuchs, Roland Bingisser, Martin Siegemund, Hans Pargger, Diana Ciardo, Olivier Dubuis, Andreas Buser, Sarah Tschudin-Sutter, Manuel Battegay, Rita Schneider-Sliwa, Karsten M. Borgwardt°, Hans H. Hirsch°, and Adrian Egli° (* = equal contribution, ° = joint supervision)
Virus Evolution 2022, 8 (1): veac002
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