A LOGICAL AND ONTOLOGICAL FRAMEWORK FOR KNOWLEDGE DISCOVERY ON GENE REGULATORY NETWORKS. CASE STUDY: BILE ACID AND XENOBIOTIC SYSTEM (BAXS)
This work aims to develop a novel computational framework for automatic or semi automatic modeling of gene regulatory networks, in which possible connections between genetic structural knowledge and other forms of knowledge available about GRNs can be explored. To perform the modeling of such networks, we consult computing services available through portals on the Internet. Some of these portals are: GeneOntology, PDB, HGNC, PathwayCommons, UniProt, PubMed, Mesh, among others. The mentioned sites provide services that make possible automatic access and can, therefore, be used to organize knowledge bases that integrate their resources. Our team has developed a prototype system that allows such an integration and the analysis of information so obtained in different modalities. In our case, the knowledge automatically gathered and modeled, expresses the identity of the objects on a network and their interactions, as well as their biological functions, biological processes and cellular components. In this sense, our proposal aims at the semantic modeling of GRNs by layers, each layer representing a standard level of description. It starts with a basic level for the model of the transcription regulatory region and goes up to a level for extra cellular objects. Our platform consolidates all the information obtained from the different sources mentioned into one integrated representation. This representation can then be used for the discovery of biological signaling pathways and the discovery of regulatory sub networks. At the moment, we are running processes to model and analyze the Bile Acid and Xenobiotic System (BAXS) and some results are already available. For instance, given a set of ligands, a couple of proteins and their related DNA sequences, it is possible: a) to discover a GRN describing how the given proteins regulate each other; and b) to produce sets of user validated pathways for the proteins under consideration. Details about our system, named biopatternsg (biopatterns searching) can be viewed at: https:/github.com/biopatternsg/biopatternsg.