The exciton binding energy is given by the electron-hole-binding state, whereas the biexciton-binding energy reflects in addition electron electron and hole hole interactions.
We consider a monomer binding and thus we assume that the binding rate is given by βn, where n is the number of proteins in the system, and the rate of switching the gene on (unbinding) is denoted by α; see Fig. 1.
The condition (Eq. 13) implies that the main contribution to the polaron binding energy is given by small values of the wave vector k such that (14).
The acceptor binding energy is given by E A = E g - E D - E DAP + e 2 4 πϵ ϵ 0 r, (2).
The probability of detecting a binding event is given by multiplying the probability of matching features and the probability of being able to detect low affinity binders.
We first consider the simplest case that there is only one target site Sij for TF i in the promoter of gene j: T F i + S i j ↔ k b k d [ T F i ⋅ S i j ] The site-specific binding affinity is given by (5) φ = C i e - E i j k T where Ci is a constant, Eij the binding free energy between TFi and the promoter of gene j, k and T are the Boltzmann constant and temperature, respectively.
Therefore, P value for binding sites prediction is given by p = ∑ i = n N N ! n !
The binding (free) energy is given by E(S) = Σ ib ε ib S ib, within the single base model [ 15, 16].
The binding strength of each position is given by a number between -1 and 1 that is drawn randomly from a uniform distribution and is scaled such that (a k ) T → a k → = 1.
For each TF binding site motif m, it is given by the following formula: where NT i(m) is the set of internal nodes of the ith-tree associated to m and depth(n) is the depth of node n in such tree.
