Abstract: Recent innovations in experimental techniques on single molecule
detection resulted in advances in the quantification of molecular noise in
several systems, and provide suitable data for defining stochastic
computational models of biological processes. Some of the latest stochastic
models of cell cycle regulation analyzed the effect of noise on cell cycle
variability. In their study, Kar et al. (Proc. Natl. Acad. Sci. USA 106,
6471–6476, 2009) found that the observed variances of cell cycle time and cell
division size distributions cannot be matched with the measured long half-lives
of mRNAs. Here, we investigate through modeling and simulation how the noise
created by the transcription and degradation processes of a key cell cycle
controller mRNA affect the statistics of cell cycle time and cell size at
division. Our model consists of an encoding of the model of Kar et al.
into a stochastic Petri net, with the extensions necessary to represent
multiple synthesis (gestation) and degradation (senescence) steps in the
regulation of mRNAs. We found that few steps of gestation and senescence of
mRNA are enough to give a good match for both the measured half-lives and
variability of cell cycle-statistics. This result suggests that the complex
process of transcription can be more accurately approximated by multi-step linear processes.
Keywords: Cell cycle, noise, stochastic Petri nets, gene expression, mRNA gestation, mRNA senescence, systems biology, yeast
