Liu LL, Brumbaugh J, Bar-Nur O, Smith Z, Stadtfeld M, Meissner A, Hochedlinger K, Michor F. 2016. Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells. Cell reports. 17(12):3395-3406. Pubmed: 28009305 DOI:S2211-1247(16)31654-0


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.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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Photo of Franziska Michor

Franziska Michor uses the tools of theoretical evolutionary biology, applied mathematics, statistics, and computational biology to address important questions in cancer research.

Photo of Alex Meissner

The Meissner laboratory uses genomic tools to study stem cell biology with a particular focus on epigenetic reprogramming.

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