On the other hand, we observe significant improvement in determinability of some regulatory weights when we compute dependent and independent confidence intervals for each parameter set by (13) and (14), respectively (see Figure 3.4 in Additional file 1).
For each parameter setting (determined by gE, gI, and the noise level), five simulations were performed.
The number of replicates considered for each parameter setting was limited by the long running time of the ILP solver.
Relative period sensitivities were integrated into the bootstrap method by calculating appropriate sensitivities for each estimated parameter set.
These test functions provide a general, but concise picture of the EEB management offered by each parameter setting strategy.
Each randomly generated parameter set is obtained by varying each of the parameters within a uniform distribution around the fiducial parameter set in Table 1 by ±5%, 15% and 50%.
Like RNAfold, RNAcofold can be used to compute DNA dimers by replacing the RNA parameter set by a suitable set of DNA parameters.
One now aims at maximizing (1) by choosing an optimal parameter set Λ.
We will now walk through this multidimensional parameter set by considering one parameter at a time.
Constant c is a smoothing parameter set by the user.
