Division time-based amplifiers for stochastic gene expression

Abstract

While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex - apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random. However, how this time-based division affects gene expression as well as how it contributes to cell-to-cell variability remains unexplored. Using a computational model combined with experimental data, we show that the cell-cycle length defined as the difference between two sequential division times can significantly impact the expression dynamics. Specifically, we find that both divisions (constant or random) always increase the mean level of mRNA and lengthen the mean first passage time. In contrast to constant division, random division always amplifies expression noise but tends to stabilize its temporal level, and unimodalizes the mRNA distribution, but makes its tail longer. These qualitative results reveal that cell division based on time is an effective mechanism for both increasing expression levels and enhancing cell-to-cell variability.

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

“…As expected, increases linearly with the average cell-cycle time duration 〈 T 〉 with longer cell cycles resulting in more accumulation of proteins. Consistent with previous findings, Eq (17) shows that the mean protein level is also affected by the randomness in the cell-cycle times [ 40 , 63 ]. For example, reduces by 25% as T changes from being exponentially distributed to periodic for fixed 〈 T 〉.…”

The mean and noise of stochastic gene transcription with cell division

“…The analytic solutions agree exactly with Gillespie stochastic simulation using plausible parameters k on = k of f = 1, µ on = 10, δ = 0.0529 [52]. Figure 4 shows the analytic solutions and the numerical solutions of mean transcript level with BR and AS, where the cell cycle obeys log-normal distribution with the mean τ = 120.…”

“…For constant cycle, we take the average in a few cycles as the steady-state mRNA mean or steady-state mRNA noise, otherwise we average the values in the time interval [200,800] as the steadystate mRNA mean or steady-state mRNA noise. The parameters k on = k of f = 1, µ on = 10, δ = 0.0529 [52]. The initial mRNA molecule number m 0 = 4.…”

“…Although there has been much work focused on cell divisions [4,11,16,17,18,24,34,38,43,50,51,52], there is little work of quantitative analysis exploring the effects of symmetric and asymmetric cell divisions as well as cell cycle on mean transcript level and transcription noise. In this paper, we consider the time-based division and merge cell division as well as a variety of inheritance regimes into a two-state model of gene transcription.…”

“…The extrinsic noise originates from the cellular environmental perturbations or from extracellular signals regulating intracellular processes. Although there is a large body of literature devoted to mean transcript level and transcription noise [5,6,12,13,14,19,28,31,32,33,46,47,48,49,54,55] even cell divisions [4,11,16,17,18,24,34,38,43,50,51,52], there is little work of quantitative analysis exploring the effects of symmetric and asymmetric cell divisions as well as cell cycle on mean transcript level and transcription noise. Here, we have constructed a theoretical model to describe the random transcription with cell division.…”