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This means that the continuous model reproduces the binding events from the RW model independently of the radial position of the RyRs.
In the first set of RW simulations, we registered the binding events from the steady-state response of a single open LCC in the cleft.
This small variation explains why the IEIs of the registered binding events from the RW model were statistically indistinguishable from those of the continuous model.
As seen in Table 2, where the results of the Student's t-tests and the KS-tests are presented, the predicted distributions of binding events from the continuous model fit the corresponding distributions of registered binding events from the RW model.
Using the central limit theorem, we compared the mean number of binding events from each RyR against the expected number of binding events from the continuous model, with a one-sample Student's t-test.
The results of the statistical goodness-of-fit tests reveal that the continuous model, for a specific parameter range, can reproduce the registered binding events from the RW simulations.
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Here, we see that the binding rates follow the number of registered binding events from Fig. 12 B, and not the predicted rate from the continuous model.
Depending on the parameters, 〈 NE〉 can well exceed 1, which increases the probability of registering two or more binding events from a single diffusive ligand.
Using this in Eq. 27, we obtained the probability of getting two or more binding events from a single diffusive ligand nearby a receptor.
Stochastic binding events from four different RyRs were registered.
In this paper, we developed a method called SeqSite to pinpoint TFBSs for both isolated and close adjacent binding events from ChIP-seq data.
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