The idea of this project is to "learn" regulated promoter models, based on a set of user-provided sequences (referred to as the positive set), using a restricted Genetic Programming framework. The restriction in this setup is that the models to be evolved are not open tree topologies (see previous iteration of the project), but chains connecting recognizers via binary connectors.
The GP framework is tasked with evolving organisms that "bind" the positive dataset preferentially over a control dataset (referred to as the negative dataset). Optimal binding energies for the organisms on each sequence are computed using a variant of the Needleman–Wunsch algorithm.