CCAIM Seminar Series – Prof. Fabian J. Theis, Wellcome Sanger Institute

Professor Fabian J. Theis is Director of Helmholtz Munich Computational Health Center and Scientific Director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI). He is a Full Professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’ as well as Associate Faculty at the Wellcome Sanger Institute.

During his academic career, Prof. Theis obtained MSc degrees in Mathematics and Physics at the University of Regensburg in 2000. He received a Ph.D. degree in Physics from the same university in 2002 and a Ph.D. in Computer Science from the University of Granada in 2003. Prof. Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network Single Cell Omics Germany (SCOG). He coordinates the Munich School for Data Science (MUDS) and is co-directing the ELLIS Munich Unit, the local hub of the ELLIS Network. Since 2020, he holds the position of Co-Chair of the Bavarian AI Council of the Bavarian Ministry for Science and Art.

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For this event, Prof. Theis is hosted by Professor Mihaela van der Schaar of the University of Cambridge, a world authority on machine learning for medicine and Director of the Cambridge Centre for AI in Medicine (CCAIM).
• Wellcome Sanger Institute: https://www.sanger.ac.uk/
• HelmholtzAI: https://www.helmholtz.ai/
• Helmholtz Munich Computational Health Center: https://www.helmholtz-muenchen.de/icb/index.html
• Cambridge Centre for AI in Medicine (CCAIM): https://ccaim.cam.ac.uk/

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Topic: Learning cellular state and dynamics in single cell genomics

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. This makes this an ideal application area for machine learning method development to understand cellular variation, contribution of particular transcripts as well as impact of perturbations.

In this talk, Prof. Theis will shortly review a recent model for dynamic RNA velocity (scVelo) as well as its extension CellRank, which his team developed to learn cellular differentiation trajectories from expression profiles. It allows users to gain insights into the timing of endocrine lineage commitment and recapitulates gene expression trends towards developmental endpoints.

While this approach focuses on individual gene expression models, recently latent space modeling and manifold learning have become a popular tool to learn overall variation in single cell gene expression. Prof. Theis will follow up with representation learning approaches to identify the gene expression manifold, and the introduce models for interpretable modeling of perturbations such as drug or genetic modification on this manifold.

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