Staff

Source: HMGU

Prof. Dr. Dr. Fabian Theis
Institute Director and Research Group Leader

Phone: +49 89 3187-43260
E-mail
Building/Room: 58a / 112

Fabian Theis uses artificial intelligence to unlock the secrets of human cells. How do they interact and what goes wrong with diseases at the cellular level? Single cell sequencing allows him and his team to analyse and model single cell heterogeneities, as well as using machine and deep learning for prediction in biology and biomedicine.

CURRICULUM VITAE

Fabian Theis leads the Institute of Computational Biology at Helmholtz Munich since 2013 and holds the "Mathematical Modeling of Biological Systems" Chair at the Department of Mathematics of TU Munich. He is also Associate Faculty at the Wellcome Trust Sanger Institute since 2019 and member and/or coordinator of various initiatives:

Sparse2big consortium, involving 8 Helmholtz Centres
SingleCellOmics Germany (SCOG) network - founder.
Munich School for Data Science (MUDS, launched 2019) - coordinator
Helmholtz AI platform (launched 2019) - scientific director
ELLIS Munich unit (launched 2020) - co-director

Fabian holds MSc degrees in Mathematics and Physics (University of Regensburg, 2000) a PhD degree in Physics from the same university (2002) and a PhD in Computer Science (University of Granada, 2003). In 2017, he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich.

He has been working as visiting researcher at the department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA) and at TUAT's Laboratory for Signal and Image Processing (Tokyo, Japan).

As a principal researcher, he led the 'Signal processing & information theory' group at the Institute of Biophysics in Regensburg, Germany. In 2006, he headed a junior research group as Bernstein fellow at the Bernstein Center for Computational Neuroscience, within the Max Planck Institute for Dynamics and Self-Organisation in Göttingen. 2007, he became working group head of CMB at the Institute of Bioinformatics at Helmholtz Munich. Two years later, he became Associate Professor for Mathematics in Systems Biology at TU Munich. From 2009 to 2014 he was also member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina) and in 2010 he was awarded an ERC starting grant.

Selected Publications

1. Bergen, V., Lange, M., Peidli, S., Wolf F.A., Theis,F.J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology. doi:10.1038/s41587-020-0591-3 (2020). For press release click here

2. Böttcher, A, Büttner, M, Tritschler, S, [...], Theis, FJ°, Lickert, H°. Wnt/PCP-primed intestinal stem cells directly differentiate into enteroendocrine or Paneth cells. accepted at Nature Cell Biology (2020)

3. Sachs S, Bastidas-Ponce, A, Tritschler, S, [...]., Tschöp, MH, Theis, FJ°, Hofmann, SM°, Müller, TD°, Lickert, H°. Targeted pharmacological therapy restores β-cell function for diabetes remission. Nature Metabolism 2, 192–209 (2020). For press release click here

4. Lotfollahi, M, Wolf, FA and Theis, FJ. scGen predicts single-cell perturbation responses. Nature Methods 16, 715–721 (2019). For press release click here

5. Fischer, DS, Fiedler, AK, Kernfeld, E, Genga, RM, Hasenauer, J, Maehr, R., Theis, FJ. Inferring population dynamics from single-cell RNA-sequencing time series data. Nature Biotechnology 37, 461–468 (2019). For press release click here

6. Buettner, M, Miao, Z, Wolf, FA, Teichmann, SA°, Theis, FJ°. A test metric for assessing single-cell RNA-seq batch correction. Nature Methods 19, 43–49 (2019)

7. Eraslan, G, Simon, L, Mircea, M, Mueller, NS, Theis, FJ. Single cell RNA-seq denoising using a deep count autoencoder. Nature Communications 10, 390 (2019)

8. Wolf, F, Angerer, P, Theis, FJ. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology 19, 15 (2019). (ranked most cited paper that year from Gen Biol). For press release click here

9. Buggenthin, F, Buettner, F, Hoppe, PS, Endele, M, Kroiss, M, Strasser, M, Schwarzfischer, M, Loeffler, D, Kokkaliaris, KD, Hilsenbeck, O, Schroeder, T°, Theis, FJ°, Marr, C°. Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods 14 403–406 (2017). For press release click here

10. Haghverdi, L, Buettner, M, Wolf FA , Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848 (2016). For press release click here

° joint corresponding authors