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A Practicable Method of Tuning the Noise Intensity at Protein Level.
Lo, Shih-Chiang; You, Chao-Xuan; Shu, Che-Chi.
Afiliação
  • Lo SC; Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
  • You CX; Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
  • Shu CC; Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan.
J Comput Biol ; 27(9): 1452-1460, 2020 09.
Article em En | MEDLINE | ID: mdl-32058806
ABSTRACT
The expression of genes is inevitably subject to intracellular noise. Noise, for some regulatory networks, is constructive but detrimental to many others. The intensity of the noise is a determinant factor and the method of tuning it is of great value. In this study, we illustrated that the transcriptional delay in an incoherent feedforward loop (FFL) grants the target protein modulation the intensity of noise. Remarkably, for a wide range, the coefficient of variation (COV) of the target protein appeared to be about linear to the time span of the transcriptional delay. Without a noise-buffering method, the COV of the target protein is 0.455. While applying incoherent FFL, the COV reduced to 0.236. Then, it changed from 0.236 to 0.630 as the transcriptional delay raised from 0 to 1000 seconds. If we further increased the delay out of the linear range, the COV finally reached 0.779. In addition, we incorporated the distribution of the transcriptional delay in the delay stochastic simulation algorithm. This distribution is based on the experimental observation in the literature. The outcome suggested that the distributed delay slightly improved the ability of tuning noise. In conclusion, we demonstrated a noise-tuning method that altered only the intensity of noise without changing the deterministic steady-state behaviors. It is ready to be applied to various systems in the field of synthetic biology.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcrição Gênica / Biossíntese de Proteínas / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: J Comput Biol Assunto da revista: BIOLOGIA MOLECULAR / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Taiwan

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transcrição Gênica / Biossíntese de Proteínas / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: J Comput Biol Assunto da revista: BIOLOGIA MOLECULAR / INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Taiwan