Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data

• Published in: BMC systems biology Vol. 12; no. Suppl 3; p. 32
• Main Authors: Miannay, Bertrand, Minvielle, Stéphane, Magrangeas, Florence, Guziolowski, Carito
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.03.2018; BioMed Central Ltd
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Cancer; Cellular and Medical Topics; Computational Biology/Bioinformatics; Gene expression; Genetic aspects; Life Sciences; Multiple myeloma; Physiological; Simulation and Modeling; Systems Biology; Regulatory network modeling; Answer set programming; Omic data integration
• ISSN: 1752-0509; 1752-0509
• DOI: 10.1186/s12918-018-0551-4

Background 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, however our approach does not require a prior species selection according to their gene expression level. Results 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 confrontation 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 apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. Conclusion We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.