Prof. Dr. Fabian J. Theis
Academic Career and Research Areas
Fabian Theis (b. 1976) conducts research in the field of computational biology. He focuses on machine learning methods applied to biological questions, in particular for modeling single cell heterogeneities and multi-omics data integtration in the context of systems medicine. He received PhD degrees in physics and computer science in 2002 and 2003. After working as a postdoctoral research fellow in Regensburg, Tokyo and Tallahassee, he took up a position as a Bernstein fellow at the Max-Planck Institute for Dynamics and Self-Organisation in Göttingen. He later joined the Helmholtz Zentrum Munich, initially as a group leader at the Institute for Bioinformatics and Systems Biology. Since 2013 he has been director of the Institute of Computational Biology at the Helmholtz Zentrum Munich and full professor for biomathematics, holding the chair “Mathematical modeling of biological systems”, in TUM’s Department of Mathematics.
- m4 Award: Förderung des Projekts “KNOWING-turning big data into personalized therapies”, Wirtschaftsministerium Bayern (2015)
- ERC-Starting Grant 'Latent Causes' (2010)
- Mitgliedschaft in der ‘Jungen Akademie’ (an der Berlin-Brandenburgischen Akademie der Wissenschaften und der Deutschen Akademie der Naturforscher Leopoldina) (2009)
- Heinz Maier-Leibnitz Preis, DFG (2006)
- Promotionspreis 'Kulturpreis E.ON Bayern' (2003)
Key Publications (all publications)
Blasi T, Hennig H, Summers HD, Theis FJ et al: “Label-free cell cycle analysis for high-throughput imaging flow cytometry”. Nat. Commun. 7. 2016; 10256.
Filipczyk A , Marr C, Hastreiter S, Feigelman J, Theis FJ, Schroeder T: “Network plasticity of pluripotency transcription factors in embryonic stem cells”. Nat. Cell Biol. 2015; 17: 1235-1246.
Haghverdi L, Buettner F, Theis FJ: “Diffusion maps for high-dimensional single-cell analysis of differentiation data”. Bioinformatics. 2015; 31(18): 2989-2998.
Bajikar SS, Fuchs C, Roller A, Theis FJ, Janes KA: “Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles”. PNAS. 2014; 111(5): E626-35.
Sass S, Buettner F, Mueller NS, Theis FJ: “A modular framework for gene set analysis integrating multilevel omics data”. Nucleic Acids Res. 2013; 41(21): 9622-9633.