TY - JOUR
T1 - Concentration fluctuations in growing and dividing cells
T2 - Insights into the emergence of concentration homeostasis
AU - Jia, Chen
AU - Singh, Abhyudai
AU - Grima, Ramon
A2 - Ciliberto, Andrea
N1 - Funding Information:
C. J. acknowledges support from National Natural Science Foundation of China with grant No. U1930402 and grant No. 12131005. A. S. is supported by the National Institute of Health Grant 1R01GM126557. R. G. acknowledges support from the Leverhulme Trust (RPG-2020-327). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2022 Jia et al.
PY - 2022/10/4
Y1 - 2022/10/4
N2 - Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.
AB - Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.
UR - https://github.com/chenjiacsrc/Concentration-homeostasis
U2 - 10.1371/journal.pcbi.1010574
DO - 10.1371/journal.pcbi.1010574
M3 - Article
SN - 1553-734X
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 10
M1 - e1010574
ER -