Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives
Fig 3
Noisy cell-cycle durations amplify protein noise differences between single-cell and population perspectives.
(A) (top:) The cell-cycle duration is a random variable following an arbitrary distribution. Within the cell cycle, the protein concentration evolves deterministically as per the ordinary differential equation (15). (bottom:) During mitosis, protein molecules are randomly segregated among daughters resulting in differences in the inherited concentration. Different shades of green represent different levels of protein concentration. (B) (left:) Trajectories of protein concentration in an expanding cell colony, where jumps represent randomness in protein partitioning among daughters during cell division. The green line: a single-cell trajectory is generated by randomly choosing one of the two daughter cells (red lines) after each division event. The light brown lines represent other descendant cells. The cell-cycle times are assumed to be exponentially distributed in this simulation. (right:) The steady-state probability density functions of the protein concentration in single-cell and population perspectives. Single-cell statistics are estimated over a 5000 independent individuals; population statistics are estimated using all cells of 2000 colonies (including sisters, progenitor and other cells). Statistics were calculated after 6 generations. (C) Effect of noise in the cell cycle time as quantified by its squared coefficient of variation (
) on the noise in the protein concentration (
). The solid line is the analytically predicted noise in the single-cell perspective as given by (18), and the dots represent noise levels computed from simulations. Mean concentrations in both models are identical
. (D) A logarithmic scale representation of the steady-state protein noise level as a function of the mean protein level, highlighting variability differences between single-cell and population perspectives. Parameters used for the plot are
,
,
.