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In-Depth Information
Biological Petri Nets
Role of mRNA Gestation and Senescence in
Noise Reduction during the Cell Cycle
Attila Csikasz-Nagy
∗
and Ivan Mura
The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Povo, Italy
ABSTRACT:
Recent innovations in experimental techniques on single molecule detection resulted in advances in the quan-
tification 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
INTRODUCTION
The noise in gene expression has been investigated extensively in the last decade [1-4]. From these
results we learned how temporal binding of the transcriptional machinery can induce bursts of mRNA
production [5], and how intrinsic and extrinsic noise can be separated [4]; there are also ideas about the
role of noise in transcriptional regulation [6-8]. Furthermore, the ways how system level interactions can
reduce noise were also discussed [9-11] and gave us some hints on how various fluctuations in biological
systems can be trimmed down by clever network wirings. Negative feedback loops, dimerization
and feed-forward loops were all suggested to be able to attenuate noise [12,13]; moreover, Pedraza
and Paulsson [10] showed that multi-step mRNA production (gestation) or removal (senescence) can
significantly decrease protein level fluctuations.
Advances in experimental techniques on single molecules [14] and large scale measurements on
the transcriptome [15] and proteome [16] of budding yeast allow researchers to establish detailed
computational models of this organism. The cell cycle regulation of
Saccharomyces cerevisiae
is one
of the most deeply investigated topic in computational systems biology [17].
After the successes of
∗
Corresponding author. E-mail:
csikasz@cosbi.eu
.