Sentence examples for binding probability of the from inspiring English sources

By bringing the obtained result into concurrence with the results of a stochastic random walk study, the kinetic constant k of the continuous reaction diffusion model could be expressed as a function of the basic physico-chemical parameters (persistence length and jump frequency of the Brownian motion, collision frequency, binding probability) of the individual molecules.

We denote by F i) the (unobserved) binding probability of the TF to this probe.

Within the two-state model, the binding probability of the factor in thermodynamic equilibrium is Here ε is the binding energy per nucleotide mismatch and ε ρ is the chemical potential measuring the factor concentration.

After learning the model parameters using positive lasso, we set β s ′ = β s / max s β s to be the binding probability of the TF to the k-mer s up to a scaling constant.

Though, we do not know the exact microscopic rate constants which determine the binding probability of the RecA filament to the target site (pbind), we can estimate that even for a recognition probability as low as approximately 1%, sliding leads to a rate enhancement in homology search by approximately 200-fold.

Given the effects β of supporting tracks, we can calculate a vector π of binding probabilities of the target protein at all probes in the ChIP data.

Assuming a linear dependence of the binding probability on the number of effective antibodies, Eq. (4) states that when irradiated approximately 50% more antiIgG are available to bind the antigen.

We use the same notation π to denote the binding probability of probes in the ChIP data, for which significant peaks are to be called, where the π used in the previous section corresponds to the training probes that are used to fit the logistic regression model.

Tregear et al. concluded that the binding probability of cross-bridges to the target zone followed a Gaussian distribution that depended on the axial offset between target-zone center and the shelf of cross-bridge origin (Fig. 9A).

We then apply a logistic regression model to compute the binding probability of each probe being occupied by the target protein, using the binned supporting data as predictors and a list of known or highly probable binary binding sites of the protein as the response.

In our analysis, we primarily focused on estimating the binding probability of a TF to either the whole analyzed promoter or at single base pair resolution (using the Bayesian method).