Constraints On Signaling Networks Logic Reveal Functional Subgraphs On Multiple Myeloma OMIC Data

Abstract

The integration of gene expression profiles (GEPs) and large-scale biological networks derived from Pathways Databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic but our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this analysis independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We applied our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph linked Multiple Myeloma (MM) genes to known receptors for this blood cancer.