A new organoid-on-chip platform robustly mimics the key features of human pancreas development. This is a milestone on the way to being able to diagnose pancreatic cancer at an early stage in the future. The study was conducted by an interdisciplinary team of researchers from Helmholtz Zent ... more
Dr. Carsten Marr
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt GmbH
Carsten Marr, born in 1977, received his diploma in general physics from the Technische Universität München in 2002. He wrote his diploma thesis at the Max-Planck-Institute for Quantum Optics, Garching, Germany, and in 2003 visited the Quantum Information and Quantum Optics Theory Group at Imperial College London. In 2004, he received a PhD fellowship from the Technische Universität Darmstadt and joined the Bioinformatics Group there, working on dynamical processes on graphs and biological networks. He visited the Center for Complex Systems and Brain Sciences, Florida Atlantic University, USA, and Jacobs University Bremen, Germany during his PhD. After receiving his PhD from Technische Universität Darmstadt in 2007 he joined Jacobs University Bremen as a postdoctoral fellow, focusing on gene regulatory networks. In 2008 he joined the Helmholtz Zentrum München as a PostDoc in the Institute for Bioinformatics and Systems Biology, working on the quantification, analysis and modelling of stem cell data. In 2011, he worked on pluripotency and stochastic descriptions of gene expression at the University of Edinburgh with a stipend from the German Research Foundation (DFG). Since 2013, he has been deputy director and group leader at the Institute of Computational Biology.
In 2017, Marr received the Erwin-Schrödinger Prize and the CSB2 Prize in Systems Biology for his interdisciplinary contributions to the quantification of single blood stem cells. In 2019, he received an ERC Consolidator Grant for establishing Computational Hematopathology.
Marr’s research focuses on the application of computational methods to understand cellular decision making in health and disease.
- Mechanistic mathematical models to fit biomedical data and predict stem cell kinetics
- Deep learning algorithms to improve single cell profiling and image-based diagnostics