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Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths

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  • Chunhe Li
  • Jin Wang

Abstract

Cellular reprogramming has been recently intensively studied experimentally. We developed a global potential landscape and kinetic path framework to explore a human stem cell developmental network composed of 52 genes. We uncovered the underlying landscape for the stem cell network with two basins of attractions representing stem and differentiated cell states, quantified and exhibited the high dimensional biological paths for the differentiation and reprogramming process, connecting the stem cell state and differentiated cell state. Both the landscape and non-equilibrium curl flux determine the dynamics of cell differentiation jointly. Flux leads the kinetic paths to be deviated from the steepest descent gradient path, and the corresponding differentiation and reprogramming paths are irreversible. Quantification of paths allows us to find out how the differentiation and reprogramming occur and which important states they go through. We show the developmental process proceeds as moving from the stem cell basin of attraction to the differentiation basin of attraction. The landscape topography characterized by the barrier heights and transition rates quantitatively determine the global stability and kinetic speed of cell fate decision process for development. Through the global sensitivity analysis, we provided some specific predictions for the effects of key genes and regulation connections on the cellular differentiation or reprogramming process. Key links from sensitivity analysis and biological paths can be used to guide the differentiation designs or reprogramming tactics.Author Summary: Cellular differentiation and reprogramming have been extensively studied using experimental methods. We developed a landscape and kinetic path approach to explore the global stability of a stem cell developmental network. The cell fates are quantified by the basins of attractions of the underlying landscape. The developmental process can be quantitatively described and uncovered by the biological paths on the landscape from the progenitor state to the differentiation state. This allows us to trace the underlying detailed kinetic process and obtain the recipe for engineering differentiation and reprogramming. By quantifying the landscape topography by the barrier heights and dynamic transition speed, we can evaluate the stability and kinetics of cell fate decision making process of the development and reprogramming. The global sensitivity analysis provided predictions about the effects of the key genes and regulation links of the network on the stability of differentiation and reprogramming process. This can be tested in the experiments. Results from sensitivity analysis and biological paths acquired can be used to guide the differentiation designs or reprogramming tactics.

Suggested Citation

  • Chunhe Li & Jin Wang, 2013. "Quantifying Cell Fate Decisions for Differentiation and Reprogramming of a Human Stem Cell Network: Landscape and Biological Paths," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-14, August.
    • Handle: RePEc:plo:pcbi00:1003165
    DOI: 10.1371/journal.pcbi.1003165
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    1. Shinya Yamanaka, 2009. "Elite and stochastic models for induced pluripotent stem cell generation," Nature, Nature, vol. 460(7251), pages 49-52, July.
    2. Thomas Graf & Tariq Enver, 2009. "Forcing cells to change lineages," Nature, Nature, vol. 462(7273), pages 587-594, December.
    3. Chunhe Li & Erkang Wang & Jin Wang, 2011. "Potential Landscape and Probabilistic Flux of a Predator Prey Network," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-9, March.
    4. Shinya Yamanaka & Helen M. Blau, 2010. "Nuclear reprogramming to a pluripotent state by three approaches," Nature, Nature, vol. 465(7299), pages 704-712, June.
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    Cited by:

    1. Rowan D Brackston & Eszter Lakatos & Michael P H Stumpf, 2018. "Transition state characteristics during cell differentiation," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-24, September.

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