What do we do?
Our research at the Computational Single Cell Biology Group aims at elucidating the composition of heterogeneous cell populations and how these implement function in the context of cancer and immune biology. To accomplish this task, we build on concepts from statistics, machine learning and mathematical optimization to develop probabilistic approaches to describe biological systems, learn these descriptions from data, and design experiments to validate hypotheses following computational analyses. Our research can be used to pinpoint therapeutic targets with a perspective to designing drugs.
During the course of 2016, our team overhauled CellCnn – a sensitive means to detect rare disease-associated cell subsets via representation learning. The team also developed Reactionet Lasso: structure learning for stochastic reaction networks and STILT, a particle filter based Bayesian model selection approach for single cell time-lapse imaging experiments.
Main publications 2016
- Klimovskaia A, Ganscha S, Claassen M, Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series, PLOS Computational Biology, 2016
- Feigelman JS, Ganscha S, Hastreiter S, Schwarzfischer M, Filipczyk A, Schroeder T, Theis FJ, Marr C*, Claassen M*, Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells, Cell Systems, 2016