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  Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-DF52-A Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-DF53-8
Genre: Journal Article

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Liu.pdf (Publisher version), 3MB
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© 2016 The Authors. Published by Elsevier Inc.

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 Creators:
Liu , Lin L. , Author
Brumbaugh , Justin , Author
Bar-Nur , Ori , Author
Smith , Zachary , Author
Stadtfeld , Matthias , Author
Meissner, Alexander1, 2, Author              
Hochedlinger, Konrad , Author
Michor, Franziska , Author
Affiliations:
1Dept. of Genome Regulation (Head: Alexander Meissner), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2379694              
2Department of Stem Cell and Regenerative Biology, Cambridge, MA 02138, USA, ou_persistent22              

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Free keywords: birth-death transition processes; induced pluripotent stem cells; mathematical/probabilistic modeling; reprogramming
 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.

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Language(s): eng - English
 Dates: 2016-12-20
 Publication Status: Published online
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.celrep.2016.11.080
 Degree: -

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Title: Cell Reports
Source Genre: Journal
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Pages: - Volume / Issue: 17 (12) Sequence Number: - Start / End Page: 3395 - 3406 Identifier: Other: 2211-1247
CoNE: /journals/resource/2211-1247