In the following we describe certain assumptions we included in our model, either for simplicity or due to a lack of experimental data .

We further regard the nucleo-cytoplasmic shuffling of fi-catenin in our model as a simple diffusion process with rate constants based on experimental data , cf.

More details about the experimental data and in vitro experimentation are described in the previous Section and in the Material and Methods Section respectively.

In the following we describe experimental data , retrieved from ReNcell VM197 human progenitor cells.

Computational modeling is increasingly applied to derive or test hypotheses, that in most cases arise from experimental data .

However, we also have to verify whether the model predictions are still in accordance with experimental data when it comes to perturbations, like raft disruption.

The model is based on experimental data as well as literature values and has been extensively validated against in-vitro and in-silico data under a Wide range of varying conditions.

In this article, we propose a novel method to estimate these quantities from experimental data , and thus assess the validity of the standard model of percept formation.

However, most likely, any chosen model will be (1) difficult to fit rigorously on the basis of experimental data , (2) subject to pathological situations when extrapolations fail to produce the correct predictions.

35—37 to experimental data .

Our study describes percept formation within a full sensory population, and proposes novel methods to estimate its characteristic readout scales on the basis of realistic samples of experimental data .

Variables and notations: experimental data .

Raw experimental data 3 Stimulus—a varying scalar value on each trial so Threshold stimulus value in the 2AFC task 0* Animal choice—binary report on each trial r,-(t) Spike train from neuron i in a given trial 02 Stimulus variance across trials

Finally, we discuss the scope and the limitations of our method, and how it can be applied to real experimental data .

Finally, we detail our methodology to empirically estimate the quantities used in this article, from limited amounts of experimental data .

In this final part of the Methods, we provide additional information for applying our inference method (Case 2) to experimental data .

As such it makes a number of hypotheses which should be understood when applying our methods to real experimental data .

This goal was achieved by constructing an integrative model of monocyte behavior based on experimental data .

1C): 1) experimental measurement of the responses of TEM differentiated in vitro to a set of ligands, 2) construction of a dynamic regulatory network based on these experimental data , 3) in silico prediction of the treatments altering TEM behavior, 4) experimental validation of computationally predicted treatments using ivdTEM and 5) validation the best predicted treatments in patient TEM (Fig.

Construction of dynamical models from the experimental data using TEM differentiated in vitro

We used TEM differentiated in vitro to derive a dynamical regulatory network from experimental data obtained with a selected number of li-gands (Fig.

This goal was achieved by constructing an integrative and predictive model of TEM behavior based on experimental data .

The corresponding experimental data and all P values are available in 82 Table.

The corresponding experimental data are available in 82 Table.

In order to quantitatively validate and compare our method with this well-established approach, we systematically analyzed experimental data using both methods.

Note that the displacements and not tractions constitute the experimental data in TFM.

(B) Experimental data for a representative U2OS-cell.

We give a detailed description of the method and demonstrate the application to experimental data .

Based on the additional experimental data , the model can achieve a more detailed traction map.

Having observed a rate dependency in the CV of PCs in our experimental data (81 Fig, panel C) we asked Whether spiking variability could account for the flat PRC profile observed at low firing rates.

In our experimental data set, the inter-spike intervals distributions displayed a significant inverse correlation of the coefficient of variation (CV) with the firing rate (i.e., Pearson’s r = —0.4, p < 10—6, and a slope of —0.25/kHz—Sl Fig, panel B) regardless of whether open or closed-loop methods used.

However, while their main focus was to introduce and test a novel technique for the PRC estimation, their experimental data set consisted of a small population of 16 cells.