We introduce a new unobserved factor, which describes the pathway activity of single cells .

We apply our model to gene silencing screens, investigating human rhino virus infection of single cells from microscopy imaging features.

A limitation of the current model formulation is the assumption of independent single cell observations.

Especially in the light of single cell data sets, which show large heterogeneity among individual observations, our approach is beneficial.

Such variation on the single cell level needs to be accounted for.

Given single cell data D = (dekc) with c = 1, .

In the following, we show improved network inference with NEMiX in simulations and then infer networks of high accuracy, from single cell gene silencing experiments.

For the NEM approach we had to summarize the single cell observations to the gene level.

., K}, With single cell observations c E {1, .

Regarding single cells as independent, for a given network structure (I), the local likelihoods further decompose into

Here, we present a mechanistic single cell growth model that is able to predict cell growth and division timing in budding yeast populations.

Our approach is to model single cells that are capable of growth and division, and grow them in in sil-ico cell cultures.

We estimate five out of 14 parameters because to a certain degree we anticipated parameter correlations, over-fitting or under-determination of parameters since the nature of the data (population averaged) and the parameters ( single cell ) are distinct.

The volume trajectory for a single cell is biphasic With altered growth rates dependent on cell cycle stage as observed experimentally as well (Fig 1B) [15, 40—42].

Single cell Model-2 in SBML format.

The model itself is a single cell model that can grow and divide (SI File).

To model entire asynchronously growing cell cultures, however, we developed an algorithm to simultaneously simulate a growing ensemble of the single cell models [32].

Single cell data has shown in detail that there is little difference for time in S-GZ-M between daughters and mothers (Table 3) [14].

The Phase Response Curve (PRC), a simple input-output characterization of single cells , can provide insights into individual and collective properties of neurons and networks, by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials, while a neuron is firing tonically.

The phase response curve allows us to discuss the impact of single cell properties on net-work-level phenomena, as a putative way to relay strong inhibition downstream (by synchroni-sation of PCs).

We provide a thorough characterization of single cell properties that might be involved in synchronization, hence contributing for the observed phenomena.

2B, is comparable to that measured for single cells , revealing and confirming the same marked smooth dependency on the firing rate (Fig.

Black and gray curves the linear fit for all experiments and for the single cell respectively.

Neurons in the brain have elaborate dendritic morphologies, hosting a variety of nonlinear channels that give way to single cell computation.

Using this data, we create a detailed single cell model and simulate synaptic input.

In this work, we explored the biophysical mechanism and computation in single cells that accompanies such bursting.

How might such single cell computation be involved in visual processing?

Third, our method analyzes bulk populations of cells to identify changes in cell state ratios, rather than analyzing large numbers of single cells .

To directly examine this possibility we assessed Whether single cells from RUNXl inhibited spheres could form tissue rudiments When seeded into collagen.

We seeded cells With dox to form RUNXl inhibited spheres, harvested and dissociated the spheres by treatment With collagenase and trypsin, and then reseeded single cells into collagen With or Without dox.