Population level oscillations in mRNA concentrations as predicted by our cycle model (Fig 5E) have also been observed experimentally (Fig 6D).

Some of the co-regulators were shown to oscillate at the population level with a 60-min period.

For multiple cells, this led to the prediction of population level transient oscillation in mRNA levels (Fig 5E).

If no mRNA degradation occurred on the considered timescale the bursts led to stepwise accumulation of mRNA in individual cells (Fig 5D) as well as on the cell population level (Fig 5F).

The mechanism we propose for the transient oscillations at the population level is entirely cell-autonomous, i.e.

For transcriptional cycling, we propose that the transient cyclic dynamics at the population level are the consequence of a simultaneous start and accurate durations.

In some cases transcription dynamics at the cell population level proceeds in an oscillatory fashion [23—28] at frequencies between 30 and 60 min [24, 27], i.e.

Precise transcription cycle times, despite inherent molecular noise, can cause transient transcriptional oscillations at the population level

The overall waiting time distribution to complete Nth cycle becomes narrower with an in-we consider the noise in the timing of the end of the N—th cycle relative to the mean duration of become asynchronous with increasing number of completed transcription cycles, and that phase-noise in the Nth cycling time increases linearly with N. At the same time, it shows that more transitions per transcription cycle, 11, tend to prolong the persistence of population level synchrony.

Fig 4D shows that population level transcriptional cycles are observable for around 100 min and decrease in amplitude over time due to de-synchronization as expected.

As a consequence of this cellular heterogeneity, knockdowns result in variable effects among cells and lead to weak average phenotypes on the cell population level .

Nested effects models, a method tailored to reconstruct signaling networks from high-dimensional readouts of gene silencing experiments, have so far been only applied on the cell population level .

On the population level , the signal is potentially confounded as it is only contained in part of the observations.

To assess the impact that pathway disruption has on the cell population level , we ran the simulations on a standard NEM using the log-likelihood model introduced in [23].

We use the model to show that biomass dependent accumulation of cyclins to a threshold results in size homeostasis on the population level and growth rate dependent size adaptation in G1 (Fig 2), as seen in vivo [15].

In silica cultures generated With Model-1 and Model-2 show size homeostasis on the population level (Figs 2A and SI).

It has been reported that a growth rate dependent sizer can prevent large fluctuation of G1 length to reduce the generation time on the population level [15].

Interestingly, this does not lead to a reduction in generation time on the population level (810 and $11 Figs).

Although this model considers the population level response to an antibiotic, there is a significant amount of gene-expression noise at the single cell level [66,67].

For 2.3 < 610 < 26, the more resistant subpopulation recovers faster and determines the population level recovery time.

Once 2.3 < 610 < 26, then 112 recovers faster and determines the population level recovery time.