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Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise

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  • Cheng Lv
  • Xiaoguang Li
  • Fangting Li
  • Tiejun Li

Abstract

Genetic switching driven by noise is a fundamental cellular process in genetic regulatory networks. Quantitatively characterizing this switching and its fluctuation properties is a key problem in computational biology. With an autoregulatory dimer model as a specific example, we design a general methodology to quantitatively understand the metastability of gene regulatory system perturbed by intrinsic noise. Based on the large deviation theory, we develop new analytical techniques to describe and calculate the optimal transition paths between the on and off states. We also construct the global quasi-potential energy landscape for the dimer model. From the obtained quasi-potential, we can extract quantitative results such as the stationary distributions of mRNA, protein and dimer, the noise strength of the expression state, and the mean switching time starting from either stable state. In the final stage, we apply this procedure to a transcriptional cascades model. Our results suggest that the quasi-potential energy landscape and the proposed methodology are general to understand the metastability in other biological systems with intrinsic noise.

Suggested Citation

  • Cheng Lv & Xiaoguang Li & Fangting Li & Tiejun Li, 2014. "Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0088167
    DOI: 10.1371/journal.pone.0088167
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    1. Ertugrul M. Ozbudak & Mukund Thattai & Han N. Lim & Boris I. Shraiman & Alexander van Oudenaarden, 2004. "Multistability in the lactose utilization network of Escherichia coli," Nature, Nature, vol. 427(6976), pages 737-740, February.
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    Cited by:

    1. Hao Ge & Pingping Wu & Hong Qian & Xiaoliang Sunney Xie, 2018. "Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.
    2. 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.
    3. Ye, Yusong & Yang, Zhuoqin & Zhang, Zili, 2016. "Opinion evolution and rare events in an open community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1178-1188.

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