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We estimated binding probabilities in two different growth conditions, P c 1(B) and P c 2(B), as well as the response of the gene expression levels to this change in growth condition, from c1 to c2.
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A histogram of the estimated binding probabilities in Figure 14 (a), for example, shows strong bias towards weak binding probabilities.
We note that the fitness F is measured relative to that of a phenotype with zero binding probability in any state α.
We calculated binding probabilities based on three types of evidence under the normal growth condition (YPD) and the six stress conditions described in Table 1.
The probability in two dice games is easy to compute.
This data fusion step can be performed in two ways: either use an approach where binding probabilities are estimated separately from ChIP-chip and sequence data and then combined (see [49]), or incorporate high-resolution ChIP-chip binding data (see [9]) directly into our model.
We also contribute a model of bimolecular binding probabilities that can be used in RW simulations.
Therefore, the histogram of binding probabilities can, for example, be considered as a mixture of two exponentially decreasing (no binding) and increasing (binding) distributions.
Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.
Finally, the binding probabilities (probability to record an unbinding event in one force-distance cycle) from several experiments were quantified.
It's only binding in two states, California and Florida.
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