Stable maintenance of hidden switches as a strategy to increase the gene expression stability
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
KAUST Grant NumberBAS/1/1624-01
Embargo End Date2021-07-14
Permanent link to this recordhttp://hdl.handle.net/10754/667208
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AbstractIn 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.
CitationKuwahara, 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
SponsorsWe 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.
JournalNature Computational Science
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