Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes

Soltani, Mohammad; Vargas-García, César Augusto; Antunes, Duarte J.; Singh, Abhyudai

doi:10.1371/journal.pcbi.1004972

Cited by 132 publications

(135 citation statements)

References 106 publications

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“…We model each cell cycle phase independently as a series of steps, with total number of k steps, and a progression rate (λ) through each step (Figure 2G, see Method Details ). Therefore, each step of a cell cycle phase can be described as a Poisson process, and the total cell cycle phase duration can be modeled as an Erlang distribution (Soltani et al , 2016). Because the model is characterized by a sequence of steps with the same rate, it is straightforward to introduce rate changes into cell cycle progression.…”

Section: Resultsmentioning

confidence: 99%

Orchestration of DNA Damage Checkpoint Dynamics across the Human Cell Cycle

et al. 2017

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Although molecular mechanisms that prompt cell cycle arrest in response to DNA damage have been elucidated, the systems-level properties of DNA damage checkpoints are not understood. Here, using time-lapse microscopy and simulations that model the cell cycle as a series of Poisson processes, we characterize DNA damage checkpoints in individual, asynchronously proliferating cells. We demonstrate that within early G1 and G2, checkpoints are stringent: DNA damage triggers an abrupt, all-or-none cell cycle arrest. The duration of this arrest correlates with the severity of DNA damage. After the cell passes commitment points within G1 and G2, checkpoint stringency is relaxed. By contrast, all of S phase is comparatively insensitive to DNA damage. This checkpoint is graded: instead of halting the cell cycle, increasing DNA damage leads to slower S-phase progression. In sum, we show that a cell’s response to DNA damage depends on its exact cell cycle position and that checkpoints are phase-dependent, stringent or relaxed, and graded or all-or-none.

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Section: Resultsmentioning

confidence: 99%

Orchestration of DNA Damage Checkpoint Dynamics across the Human Cell Cycle

et al. 2017

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“…These events could be, for example, the sequential degradation of proteins (Coleman et al, 2015) or the stepwise accumulation of a molecular factor (Ghusinga et al, 2016;Garmendia-Torres et al, 2018) that must reach a threshold in order to complete the phase. The total amount of time needed to complete all steps in the phase has an Erlang distribution (Soltani et al, 2016). This model does not claim that each cell cycle phase is, in actuality, merely a series of exactly k steps.…”

Section: Each Cell Cycle Phase Follows An Erlang Distribution With a mentioning

confidence: 99%

Evidence that the human cell cycle is a series of uncoupled, memoryless phases

Chao

Fakhreddin

Shimerov

et al. 2019

Molecular Systems Biology

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The cell cycle is canonically described as a series of four consecutive phases: G1, S, G2, and M. In single cells, the duration of each phase varies, but the quantitative laws that govern phase durations are not well understood. Using time‐lapse microscopy, we found that each phase duration follows an Erlang distribution and is statistically independent from other phases. We challenged this observation by perturbing phase durations through oncogene activation, inhibition of DNA synthesis, reduced temperature, and DNA damage. Despite large changes in durations in cell populations, phase durations remained uncoupled in individual cells. These results suggested that the independence of phase durations may arise from a large number of molecular factors that each exerts a minor influence on the rate of cell cycle progression. We tested this model by experimentally forcing phase coupling through inhibition of cyclin‐dependent kinase 2 ( CDK 2) or overexpression of cyclin D. Our work provides an explanation for the historical observation that phase durations are both inherited and independent and suggests how cell cycle progression may be altered in disease states.

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“…Theoretical models of stochastic gene expression have seldom considered cell division explicitly, and those which did considered a random generation time drawn from a preassigned distribution [40,41], or did not consider the time-dependence of transcription and translation rate [30,42]. The random generation time assumption is incompatible with the exponential growth of cell volume and protein numbers, because in the presence of noise it would lead to the divergence of cell size [43][44][45].…”

Section: Model Of Stochastic Gene Expressionmentioning

confidence: 99%

Homeostasis of protein and mRNA concentrations in growing cells

Lin

Amir

2018

Preprint

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Many experiments show that the concentrations of protein and mRNA fluctuate but on average are constant in growing cells, independent of the genome copy number. However, models of stochastic gene expression often assume constant gene expression rates that are proportional to the gene copy numbers, which are therefore incompatible with experiments. Here, we construct a minimal gene expression model to fill this gap. We show that (1) because the ribosomes translate all proteins, the concentrations of proteins are regulated in an exponentially growing cell volume; (2) the competition between genes for the RNA polymerases makes the gene expression rate independent of the genome number; (3) the fluctuations in ribosome level and cell density can generate a global extrinsic noise in protein concentrations; (4) correlations between mRNA and protein levels can be quantified.Despite the noisy nature of gene expression [1][2][3][4][5][6], various aspects of single cell dynamics, such as volume growth, are effectively deterministic. Recent single-cell measurements show that the growth of cell volumes is often exponential. These include bacteria [7][8][9][10], budding yeast [10][11][12][13][14] and mammalian cells [10,15]. Moreover, the mRNA and protein numbers are on average proportional to the cell volume throughout the cell cycle [16][17][18][19][20]. Therefore, the homeostasis of mRNA concentration and protein concentration is maintained in an exponentially growing cell volume. The exponential growths of mRNA and protein number indicate dynamical transcription and translation rates proportional to the cell volume, and also independent of the genome copy number. However, current gene expression models often assume a fixed cell volume with constant transcription and translation rates [1,5,[21][22][23], which are proportional to the gene copy number. Therefore, fixed cell volume models fail to reveal how cells keep constant mRNA and protein concentrations as the cell volume grows and the genome is replicated.The homeostasis of protein and mRNA concentrations imply that there must be a regulatory mechanism in place to prevent the accumulation of noise over time and to maintain a bounded distribution of concentrations. The goal of this work is to identify such a mechanism by developing a genome-wide coarse-grained model taking into account explicitly cell volume growth. We will consider an idealized cell in which genes are constitutively expressed for simplicity. The ubiquity of homeostasis suggests that the global machineries of gene expression, RNA polymerases (RNAPs) and ribosomes, should play a central role within the model. Indeed, as we will show later, the exponential growth of cell volume, protein and mRNA number originates from the auto-catalytic nature of ribosomes, the limiting factor in the translational process [24][25][26]. The bounded distributions of concentrations are a result of the fact that ribosomes make all proteins. The independence of the mRNA concentration of the genome copy number is a natural resul...

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Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes

Cited by 132 publications

References 106 publications

Orchestration of DNA Damage Checkpoint Dynamics across the Human Cell Cycle

Orchestration of DNA Damage Checkpoint Dynamics across the Human Cell Cycle

Evidence that the human cell cycle is a series of uncoupled, memoryless phases

Homeostasis of protein and mRNA concentrations in growing cells

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