The goal of the thesis will be to explore existing implementations of computing gene ontology attractor enrichment analysis for fully specified Boolean Networks on sets of models sharing the same state space (i.e., so-called Partially Specified Boolean Networks). First, the student will prepare a set of well-annotated Boolean Models that will be, to some extent, abstracted to Partially-Specified Models. Second, the student will establish a workflow automating the computation of gene enrichment over a set of models generated by a given PSBN. Third, the student will propose suitable post-processing methods that will be evaluated on the prepared set of models.

The practical outcome will be the code in Python implementing the algorithms and entire pipeline (including post-processing of the results).