abstract = "Reverse engineering of transcription networks is a
challenging bioinformatics problem. Ordinary
differential equation (ODEs) network models have their
roots in the physicochemical base of these networks,
but are difficult to build conventionally. Modelling
automation is needed and knowledge discovery in data
using computational intelligence methods is a solution.
The authors have developed a methodology for
automatically inferring ODE systems models from omics
data, based on genetic programming (GP), and illustrate
it on a real transcription network. The methodology
allows the network to be decomposed from the complex of
interacting cellular networks and to further decompose
each of its nodes, without destroying their
interactions. The structure of the network is not
imposed but discovered from data, and further
assumptions can be made about the parameters' values
and the mechanisms involved. The algorithms can deal
with unmeasured regulatory variables, like
transcription factors (TFs) and microRNA (miRNA or
miR). This is possible by introducing the regulome
probabilities concept and the techniques to compute
them. They are based on the statistical thermodynamics
of regulatory molecular interactions. Thus, the
resultant models are mechanistic and theoretically
founded, not merely data fittings. To our knowledge,
this is the first reverse engineering approach capable
of dealing with missing variables, and the accuracy of
all the models developed is greater than 99percent.",