Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits
Authored by Francois Bertaux, Samuel Marguerat, Vahid Shahrezaei
Date Published: 2018
DOI: 10.1098/rsos.172234
Sponsors:
Leverhulme Trust
United Kingdom Medical Research Council
Platforms:
MATLAB
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
https://github.com/ImperialCollegeLondon/coli-noise-and-growth
Abstract
The cell division rate, size and gene expression programmes change in
response to external conditions. These global changes impact on average
concentrations of biomolecule and their variability or noise. Gene
expression is inherently stochastic, and noise levels of individual
proteins depend on synthesis and degradation rates as well as on
cell-cycle dynamics. We have modelled stochastic gene expression inside
growing and dividing cells to study the effect of division rates on
noise in mRNA and protein expression. We use assumptions and parameters
relevant to Escherichia coli, for which abundant quantitative data are
available. We find that coupling of transcription, but not translation
rates to the rate of cell division can result in protein concentration
and noise homeostasis across conditions. interestingl), we find that the
increased cell size at fast division rates, observed in E. coli and
other unicellular organisms, buffers noise levels even for proteins with
decreased expression at faster growth. We then investigate the
functional importance of these regulations using gene regulatory
networks that exhibit bi-stability and oscillations. We find that
network topology affects robustness to changes in division rate in
complex and unexpected ways. In particular, a simple model of
persistence, based on global physiological feedback, predicts increased
proportion of persister cells at slow division rates. Altogether, our
study reveals how cell size regulation in response to cell division rate
could help controlling gene expression noise. It also highlights that
understanding circuits robustness across growth conditions is key for
the effective design of synthetic biological systems.
Tags
Agent-based modelling
Replication
E. coli
bacteria
Mechanisms
Escherichia-coli
Cycle
Growth-rate
Resource-allocation
Growth rate
Single cells
Stochastic gene expression
Bistable switches
Genetic
oscillators
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