The goal of the thesis is to design an extension of AEON.py tool providing a computation workflow for gene enrichment analysis in genetic information accumulated in stable variables or in a particular attractor associated with a given Boolean Network model. The input will be the fully specified Boolean network model. 

In the theoretical part of the work, the student will discuss existing gene ontology enrichment analysis packages (including Gene Ontology web platform) and will design a computation pipeline using the selected package. The application of enrichment analysis will be also discussed in a scenario in which a set of attractors (an attractor class) is considered. In particular, suitable combination of gene enrichment results needs to be identified for specific combinations of attractors in the given attractor class.  

Based in the fact that AEON.py is Python API for the AEON attractor analysis tool set, the extension will be implemented in terms of new API functions. (or a set of attractors). The student will provide a prototype implementation of the designed pipeline. The implementation will be tested on a selected set of concrete Boolean Network models representing real biological problems. The evaluation part will include automatised post-processing of the data.