This is somewhat unexpected, because the binding probability in the continuous model is linear with respect to the [Ca2+] at a single receptor and is also constant during steady-state simulations.
A histogram of the estimated binding probabilities in Figure 14 (a), for example, shows strong bias towards weak binding probabilities.
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.
We note that the fitness F is measured relative to that of a phenotype with zero binding probability in any state α.
The second, smaller peak is located at high binding probabilities, close to the probability value 1.
For the Viterbi Algorithm, the binding probabilities are set as the transition state probabilities, while the torsion state probabilities are set as the emission probabilities.
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.
Since each probe is much longer than the motif itself (probe length is 36bp while motifs are generally less than 20bp with a typical length at 12bp [ 83]) we follow BEEML-PBM [ 16], and express the binding probability F i) as the sum of the binding probabilities over all k-mers in the probe.
We also present a new analytical model of bimolecular binding probabilities, which we use in the RW simulations and the statistical analysis.
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.
