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ABOUT FABIAN THEIS

Fabian Theis studied Mathematics and Physics and has PhDs in Physics and Computer Science. After different research stays abroad he was a Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self Organisation at Göttingen. In 2007 he became junior group leader at the Helmholtz Center Munich and associate professor at the Technical University of Munich. In 2013 he founded the Institute of Computational Biology at Helmholtz Munich and full professor at the math department at TUM. Since 2019 he is associate faculty at the Wellcome Trust Sanger Institute in Hinxton, UK. Also he is scientific director of the Helmholtz Artificial Intelligence Cooperation Unit and coordinates the Munich School for Data Science, founded in 2019.
Some of his major achievements was an ERC starting grant in 2010 and the Erwin-Schrödinger prize for interdisciplinary research in 2017.
He has a long-standing interest in Computational Biology, with specific expertise in Machine Learning in the context of single cell biology. By developing and adapting inference methods to integrate information across scales, he contributes to answering complex biological and medical questions such as stem cell decision making and impact of cellular heterogeneity in systems medicine.

Modeling cellular state and dynamics in single cell genomics

Tuesday, 8 September | 09:10 am (CEST)

Modeling cellular state as well as dynamics e.g. during differentiation or in response to perturbations is a central goal of computational biology. Single-cell technologies now give us easy and large-scale access to state observations on the transcriptomic and more recently also epigenomic level. In particular, they allow resolving potential heterogeneities due to asynchronicity of differentiating or responding cells, and profiles across multiple conditions such as time points, space and replicates are being generated.

In this talk I will shortly review scVelo, our recent model for dynamic RNA velocity, allowing estimation of gene-specific transcription and splicing rates, and illustrate its use to estimate a shared latent time in pancreatic endocrinogenesis. I will then show CellRank, a probabilistic model based on Markov chains which makes use of both transcriptomic similarities as well as RNA velocity to infer developmental start- and endpoints and assign lineages in a probabilistic manner. It allows users to gain insights into the timing of endocrine lineage commitment and recapitulates gene expression trends towards developmental endpoints.