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Exact results for gene-expression models with general waiting-time distributions.
Zhang, Jinqiang; Chen, Aimin; Qiu, Huahai; Zhang, Jiajun; Tian, Tianhai; Zhou, Tianshou.
Afiliação
  • Zhang J; School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
  • Chen A; School of Mathematics and Statistics, Henan University, Kaifeng 475004, China.
  • Qiu H; School of Mathematics and Computers, Wuhan Textile University, Wuhan 430200, People's Republic of China.
  • Zhang J; School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
  • Tian T; Key Laboratory of Computational Mathematics, Guangdong Province, Guangzhou 510275, People's Republic of China.
  • Zhou T; School of Mathematics, Monash University, Clayton 3800, Australia.
Phys Rev E ; 109(2-1): 024119, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38491572
ABSTRACT
Complex molecular details of transcriptional regulation can be coarse-grained by assuming that reaction waiting times for promoter-state transitions, the mRNA synthesis, and the mRNA degradation follow general distributions. However, how such a generalized two-state model is analytically solved is a long-standing issue. Here we first present analytical formulas of burst-size distributions for this model. Then, we derive an iterative equation for the mRNA moment-generating function, by which mRNA raw and binomial moments of any order can be conveniently calculated. The analytical results obtained in the special cases of phase-type waiting-time distributions not only provide insights into the mechanisms of complex transcriptional regulations but also bring conveniences for experimental data-based statistical inferences.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Listas de Espera / Modelos Genéticos Idioma: En Revista: Phys Rev E Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Listas de Espera / Modelos Genéticos Idioma: En Revista: Phys Rev E Ano de publicação: 2024 Tipo de documento: Article