Open AccessThe mean and noise of stochastic gene transcription with cell division
Author(s) -
Qi Wang,
Lifang Huang,
Kunwen Wen,
Jianshe Yu
Publication year - 2018
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2018058
Subject(s) - cell division , mathematics , transcription (linguistics) , statistics , messenger rna , cell cycle , negative binomial distribution , division (mathematics) , stochastic process , inheritance (genetic algorithm) , noise (video) , steady state (chemistry) , genetics , biology , gene , cell , computer science , chemistry , linguistics , philosophy , arithmetic , poisson distribution , artificial intelligence , image (mathematics)
Life growth and development are driven by continuous cell divisions. Cell division is a stochastic and complex process. In this paper, we study the impact of cell division on the mean and noise of mRNA numbers by using a two-state stochastic model of transcription. Our results show that the steady-state mRNA noise with symmetric cell division is less than that with binomial inheritance with probability 0.5, but the steady-state mean transcript level with symmetric division is always equal to that with binomial inheritance with probability 0.5. Cell division except random additive inheritance always decreases mean transcript level and increases transcription noise. Inversely, random additive inheritance always increases mean transcript level and decreases transcription noise. We also show that the steady-state mean transcript level (the steady-state mRNA noise) with symmetric cell division or binomial inheritance increases (decreases) with the average cell cycle duration. But the steady-state mean transcript level (the steady-state mRNA noise) with random additive inheritance decreases (increases) with the average cell cycle duration. Our results are confirmed by Gillespie stochastic simulation using plausible parameters.