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Statecharts for Gene Network Modeling

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  • Yong-Jun Shin
  • Mehrdad Nourani

Abstract

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks.

Suggested Citation

  • Yong-Jun Shin & Mehrdad Nourani, 2010. "Statecharts for Gene Network Modeling," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-9, February.
  • Handle: RePEc:plo:pone00:0009376
    DOI: 10.1371/journal.pone.0009376
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    References listed on IDEAS

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    1. Nicholas J. Guido & Xiao Wang & David Adalsteinsson & David McMillen & Jeff Hasty & Charles R. Cantor & Timothy C. Elston & J. J. Collins, 2006. "A bottom-up approach to gene regulation," Nature, Nature, vol. 439(7078), pages 856-860, February.
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    Cited by:

    1. Jean Peccoud & Mark Isalan, 2012. "The PLOS ONE Synthetic Biology Collection: Six Years and Counting," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.

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