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A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

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  • Teeraphan Laomettachit
  • Katherine C Chen
  • William T Baumann
  • John J Tyson

Abstract

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

Suggested Citation

  • Teeraphan Laomettachit & Katherine C Chen & William T Baumann & John J Tyson, 2016. "A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-43, May.
  • Handle: RePEc:plo:pone00:0153738
    DOI: 10.1371/journal.pone.0153738
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    References listed on IDEAS

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    1. Rajat Singhania & R Michael Sramkoski & James W Jacobberger & John J Tyson, 2011. "A Hybrid Model of Mammalian Cell Cycle Regulation," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-10, February.
    2. Stefano Di Talia & Jan M. Skotheim & James M. Bean & Eric D. Siggia & Frederick R. Cross, 2007. "The effects of molecular noise and size control on variability in the budding yeast cell cycle," Nature, Nature, vol. 448(7156), pages 947-951, August.
    3. Sina Ghaemmaghami & Won-Ki Huh & Kiowa Bower & Russell W. Howson & Archana Belle & Noah Dephoure & Erin K. O'Shea & Jonathan S. Weissman, 2003. "Global analysis of protein expression in yeast," Nature, Nature, vol. 425(6959), pages 737-741, October.
    4. Ralph Wäsch & Frederick R. Cross, 2002. "APC-dependent proteolysis of the mitotic cyclin Clb2 is essential for mitotic exit," Nature, Nature, vol. 418(6897), pages 556-562, August.
    5. Tian Hong & Jianhua Xing & Liwu Li & John J Tyson, 2011. "A Mathematical Model for the Reciprocal Differentiation of T Helper 17 Cells and Induced Regulatory T Cells," PLOS Computational Biology, Public Library of Science, vol. 7(7), pages 1-13, July.
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