Bayesian tree-based inference identifies Nanog negative autoregulation
A controversial debate in the stem cell field revolved around how the quantity of Nanog protein is regulated in murine embryonic stem cells. Together with colleagues from ETH Zürich, scientists of the Institute of Computational Biology developed an algorithm called STILT (Stochastic Inference on Lineage Trees) that evaluates time-resolved protein expression data from individual cells in which Nanog could be detected through fusion with a fluorescence protein. The results that have now been published in Cell Systems show that Nanog is regulated by a so-called negative feedback loop, which means that the more Nanog there is in the cells, the less reproduction there will be.
For more information see the original publication.
Feigelmann, J. et al. (2016). Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells. Cell Systems, 3(5), 480–490.e13. http://doi.org/10.1016/j.cels.2016.11.001
and the press release by the Helmholtz Zentrum.