Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells

Publication date: 20 December 2016
Source:Cell Reports, Volume 17, Issue 12
Author(s): Lin L. Liu, Justin Brumbaugh, Ori Bar-Nur, Zachary Smith, Matthias Stadtfeld, Alexander Meissner, Konrad Hochedlinger, Franziska Michor
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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Teaser

Liu et al. use probabilistic models to interrogate the dynamics of reprogramming from somatic cells to iPSCs. These studies demonstrate that the general two-type (or multi-type) birth-death transition process is a useful mathematical framework to investigate important biological questions, such as inferring the reprogramming rate and addressing whether cells are homogeneous in terms of properties including division rates, apoptosis rates, and reprogramming rates.


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