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A Dynamical Model of Genetic Networks for Cell Differentiation

Author

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  • Marco Villani
  • Alessia Barbieri
  • Roberto Serra

Abstract

A mathematical model is proposed which is able to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions which drive the phenomenon. On the contrary, cell differentiation is described here as an emergent property of a generic model of the underlying gene regulatory network, and it can therefore be applied to a variety of different organisms. The model points to a peculiar role of cellular noise in differentiation and leads to non trivial predictions which could be subject to experimental testing. Moreover, a single model proves able to describe several different phenomena observed in various differentiation processes.

Suggested Citation

  • Marco Villani & Alessia Barbieri & Roberto Serra, 2011. "A Dynamical Model of Genetic Networks for Cell Differentiation," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-9, March.
    • Handle: RePEc:plo:pone00:0017703
    DOI: 10.1371/journal.pone.0017703
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    References listed on IDEAS

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    2. Thomas Vierbuchen & Austin Ostermeier & Zhiping P. Pang & Yuko Kokubu & Thomas C. Südhof & Marius Wernig, 2010. "Direct conversion of fibroblasts to functional neurons by defined factors," Nature, Nature, vol. 463(7284), pages 1035-1041, February.
    3. Shinya Yamanaka, 2009. "Elite and stochastic models for induced pluripotent stem cell generation," Nature, Nature, vol. 460(7251), pages 49-52, July.
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    Cited by:

    1. Jamie X Luo & Matthew S Turner, 2012. "Evolving Sensitivity Balances Boolean Networks," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-8, May.

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