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    Stable maintenance of hidden switches as a strategy to increase the gene expression stability

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    main_clean_version (1).pdf
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    Format:
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    Description:
    Accepted manuscript
    Embargo End Date:
    2021-07-14
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    Type
    Article
    Authors
    Kuwahara, Hiroyuki cc
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    BAS/1/1624-01
    FCC/1/1976-18
    FCC/1/1976-23
    FCC/1/1976-25
    FCC/1/1976-26
    URF/1/3412-01
    URF/1/3450-01
    Date
    2021-01-14
    Embargo End Date
    2021-07-14
    Submitted Date
    2020-05-30
    Permanent link to this record
    http://hdl.handle.net/10754/667208
    
    Metadata
    Show full item record
    Abstract
    In response to severe genetic and environmental perturbations, wild-type organisms can express hidden alternative phenotypes adaptive to such adverse conditions. While our theoretical understanding of the population-level fitness advantage and evolution of phenotypic switching under variable environments has grown, the mechanism by which these organisms maintain phenotypic switching capabilities under static environments remains to be elucidated. Here, using computational simulations, we analyzed the evolution of gene circuits under natural selection and found that different strategies evolved to increase the gene expression stability near the optimum level. In a population comprising bistable individuals, a strategy of maintaining bistability and raising the potential barrier separating the bistable regimes was consistently taken. Our results serve as evidence that hidden bistable switches can be stably maintained during environmental stasis—an essential property enabling the timely release of adaptive alternatives with small genetic changes in the event of substantial perturbations.
    Citation
    Kuwahara, H., & Gao, X. (2021). Stable maintenance of hidden switches as a strategy to increase the gene expression stability. Nature Computational Science, 1(1), 62–70. doi:10.1038/s43588-020-00001-y
    Sponsors
    We thank O. Soyer and T. Gojobori for their comments on an earlier version of the manuscript. X.G. was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award numbers BAS/1/1624-01, URF/1/3412-01, URF/1/3450-01, FCC/1/1976-18, FCC/1/1976-23, FCC/1/1976-25, FCC/1/1976-26 and FCS/1/4102-02.
    Publisher
    Springer Science and Business Media LLC
    Journal
    Nature Computational Science
    DOI
    10.1038/s43588-020-00001-y
    Additional Links
    http://www.nature.com/articles/s43588-020-00001-y
    ae974a485f413a2113503eed53cd6c53
    10.1038/s43588-020-00001-y
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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