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Cell Rep. 2016 Dec 20;17(12):3395-3406. doi: 10.1016/j.celrep.2016.11.080.

Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
2
Massachusetts General Hospital Cancer Center and Center for Regenerative Medicine, Boston, MA 02114, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Department of Stem Cell and Regenerative Biology, Cambridge, MA 02138, USA.
3
Department of Stem Cell and Regenerative Biology, Cambridge, MA 02138, USA.
4
The Helen L. and Martin S. Kimmel Center for Biology and Medicine, Skirball Institute of Biomolecular Medicine, Department of Cell Biology, NYU School of Medicine, New York, NY 10016, USA.
5
Massachusetts General Hospital Cancer Center and Center for Regenerative Medicine, Boston, MA 02114, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Department of Stem Cell and Regenerative Biology, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
6
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. Electronic address: michor@jimmy.harvard.edu.

Abstract

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.

KEYWORDS:

birth-death transition processes; induced pluripotent stem cells; mathematical/probabilistic modeling; reprogramming

PMID:
28009305
PMCID:
PMC5467646
DOI:
10.1016/j.celrep.2016.11.080
[Indexed for MEDLINE]
Free PMC Article

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