Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells


Meissner,  Alexander
Dept. of Genome Regulation (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society;
Department of Stem Cell and Regenerative Biology, Cambridge, MA 02138, USA;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available

Liu, L. L., Brumbaugh, J., Bar-Nur, O., Smith, Z., Stadtfeld, M., Meissner, A., et al. (2016). Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells. Cell Reports, 17(12), 3395-3406. doi:10.1016/j.celrep.2016.11.080.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-DF52-A
Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) is typically an inefficient and asynchronous process. A variety of technological efforts have been made to accelerate and/or synchronize this process. To define a unified framework to study and compare the dynamics of reprogramming under different conditions, we developed an in silico analysis platform based on mathematical modeling. Our approach takes into account the variability in experimental results stemming from probabilistic growth and death of cells and potentially heterogeneous reprogramming rates. We suggest that reprogramming driven by the Yamanaka factors alone is a more heterogeneous process, possibly due to cell-specific reprogramming rates, which could be homogenized by the addition of additional factors. We validated our approach using publicly available reprogramming datasets, including data on early reprogramming dynamics as well as cell count data, and thus we demonstrated the general utility and predictive power of our methodology for investigating reprogramming and other cell fate change systems.