The probabilities for I1 to implement these functionalities are within the same range, unlike in C4.
The linear probability model is the easiest to implement but have limitations for prediction.
Similar observations and recommendations have been reported for Streptococcus agalactiae, i.e. a differentiation between bovine and human-adapted strains, which differ in transmission probability and in the need to implement stringent control programs [ 42].
First, it has the smallest number of categories, and will therefore be the least tedious to implement when probabilities and utilities are elicited.
The probability of circuit k to implement a given dynamic j = { G+, G-, P+(T+), P+ T-), P-(T+), P- T-)} described by the backbone sequence i can, thus, be calculated as: (11) where Ψ ijk is the number of equal dynamical outcomes j for a given backbone i in the k- th FFL and T jk the total number of backbone sequences implementing dynamic j.
The knowledge of apriori probabilities of residues is utilized to implement a probability based Distance-Aware Direct Mapping scheme.
This puts us into the position to unambiguously enumerate the probability of a given FFL to implement a certain function.
As will be shown below, we can analyse the geometrical requirements necessary in order to observe different responses to be generated and the probability for a certain FFL to implement a given function independent of the set of parameters.
For interested readers who want to implement the conditional probabilities concept in their own research, I highly suggest that real (or toy) data be included, in the very least, as supplemental material with all the data completely worked out, not just the weighted core class contributions.
Second, from a practical perspective, the use of a uniform prior probability distribution makes it straightforward to implement our approach in standard software because we can obtain the posterior density function directly from the likelihood.
