The sizes in a population of genetically identical%DIFDELCMD < {\it %%% Escherichia coli cells is known to vary, independent of growth stage. Multiple genes and proteins involved in cell division and DNA segregation are involved in regulating cell size. At the same time, physical factors such as temperature, growth rate and population size also appear to affect cell sizes. How these physical factors interact with the genetically encoded ones to produce such effects is however not clearly understood . Here, we have developed a multi-scale model of bacterial DNA replication coupled to cell division in the context of a logistically growing population. DNA replication is modeled as the stochastic dynamics of replication forks (RFs) which transition probabilistically between two states, stalled and recovered. In the model stalled RFs or incomplete DNA replication results in aberrant cell division. Simulating this model , we demonstrate that the cell-size variability of cultures depends strongly on population size and the growth phase . Our model also predicts the variability in cell sizes is independent of growth temperature between 22^o and 42^oC. To test the model, we perform experiments with%DIFDELCMD < {\it %%% E. coli strains mutant for recA, sulA and slmA, factors known to affect replication fork dynamics and coupling to cell division. Our validated model of RFstalling based cell division, can reproduce the variability in cell size distribution seen in these mutant strains, and provides a predictive tool to further examine stochastic effects in bacterial cell size regulation .
The %DIFDELCMD < {\it %%% variability in cell size of an isogenic population of Escherichia coli has been widely reported in experiment. The probability density function (PDF) of cell lengths has been variously described by exponential and lognormal functions. While temperature, population density and growth rate have all been shown to affect E. coli cell size distributions, and recent models have validated a link between growth rate and cell size through DNA replication, cell size variability is thought to emerge from growth rate variability. A mechanistic link that could distinguish the source of stochasticity, could improve our understanding of cell size regulation . Here, we have developed a population dynamics model of individual cell division based on the BCD, birth, chromosome replication and division model, with DNA replication based on the Cooper and Helmstetter (CH) multi-fork replication. In our model, stochasticity in the model arises solely from the dynamics of DNA replication forks. We model the forks as two-state systems: stalled and recovered . Our model %DIFDELCMD < {\it %%% predicts an increase in cell size variability with growth rate, consistent with previous experimental reports. We perturb the model to test the effect of increased replication fork (RF) stalling frequency, or uncoupling RF stalling from the cell-division machinery. Indeed, despite ignoring DNA and protein segregation asymmetry, the model can faithfully reproduce quantitative changes in cell size distributions. In our model, multi-fork replication produces multiplicative 'noise' and provides a mechanism linking growth rate and cell size variability .
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sizes in a population of genetically identical
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Escherichia coli
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cells is known to vary, independent of growth stage. Multiple genes and proteins involved in cell division and DNA segregation are involved in regulating cell size. At the same time, physical factors such as temperature, growth rate and population size also appear to affect cell sizes. How these physical factors interact with the genetically encoded ones to produce such effects is however not clearly understood
variability in cell size of an isogenic population of Escherichia coli has been widely reported in experiment. The probability density function (PDF) of cell lengths has been variously described by exponential and lognormal functions. While temperature, population density and growth rate have all been shown to affect E. coli cell size distributions, and recent models have validated a link between growth rate and cell size through DNA replication, cell size variability is thought to emerge from growth rate variability. A mechanistic link that could distinguish the source of stochasticity, could improve our understanding of cell size regulation
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multi-scale model of bacterial DNA replication coupled to cell division in the context of a logistically growing population. DNA replication is modeled as the stochastic dynamics of replication forks (RFs) which transition probabilistically between two states, stalled and recovered. In
population dynamics model of individual cell division based on the BCD, birth, chromosome replication and division model, with DNA replication based on the Cooper and Helmstetter (CH) multi-fork replication. In our model, stochasticity in the model arises solely from the dynamics of DNA replication forks. We model
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model stalled RFs or incomplete DNA replication results in aberrant cell division. Simulating this model , we demonstrate that the cell-size variability of cultures depends strongly on population size and the growth phase
forks as two-state systems: stalled and recovered
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also predicts the variability in cell sizes is independent of growth temperature between 22^o and 42^oC. To test the model, we perform experiments with
D
E. coli
R
strains mutant for recA, sulA and slmA, factors known to affect replication fork dynamics and coupling to cell division. Our validated model of RFstalling based cell division, can reproduce the variability
predicts an increase in cell size variability with growth rate, consistent with previous experimental reports. We perturb the model to test the effect of increased replication fork (RF) stalling frequency, or uncoupling RF stalling from the cell-division machinery. Indeed, despite ignoring DNA and protein segregation asymmetry, the model can faithfully reproduce quantitative changes
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distribution seen in these mutant strains,
distributions. In our model, multi-fork replication produces multiplicative 'noise'
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predictive tool to further examine stochastic effects in bacterial cell size regulation
mechanism linking growth rate and cell size variability