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Stochastic Stabilization of Phenotypic States: The Genetic Bistable Switch as a Case Study

Overview of attention for article published in PLOS ONE, September 2013
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Title
Stochastic Stabilization of Phenotypic States: The Genetic Bistable Switch as a Case Study
Published in
PLOS ONE, September 2013
DOI 10.1371/journal.pone.0073487
Pubmed ID
Authors

Marc Weber, Javier Buceta

Abstract

We study by means of analytical calculation and stochastic simulations how intrinsic noise modifies the bifurcation diagram of gene regulatory processes that can be effectively described by the Langevin formalism. In a general context, our study raises the intriguing question of how biochemical fluctuations redesign the epigenetic landscape in differentiation processes. We have applied our findings to a general class of regulatory processes that includes the simplest case that displays a bistable behavior and hence phenotypic variability: the genetic auto-activating switch. Thus, we explain why and how the noise promotes the stability of the low-state phenotype of the switch and show that the bistable region is extended when increasing the intensity of the fluctuations. This phenomenology is found in a simple one-dimensional model of the genetic switch as well as in a more detailed model that takes into account the binding of the protein to the promoter region. Altogether, we prescribe the analytical means to understand and quantify the noise-induced modifications of the bifurcation points for a general class of regulatory processes where the genetic bistable switch is included.

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Mendeley readers

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Geographical breakdown

Country Count As %
Spain 3 6%
United Kingdom 2 4%
United States 2 4%
Canada 1 2%
Unknown 43 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 45%
Researcher 10 20%
Professor 4 8%
Student > Bachelor 3 6%
Student > Master 2 4%
Other 3 6%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Biochemistry, Genetics and Molecular Biology 9 18%
Physics and Astronomy 9 18%
Engineering 5 10%
Medicine and Dentistry 2 4%
Other 5 10%
Unknown 8 16%