title: Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches.
abstract: A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Isar
authors.initials: I
authors.lastname: Nassiri
authors.forename: Ali
authors.initials: A
authors.lastname: Masoudi-Nejad
authors.forename: Mahdi
authors.initials: M
authors.lastname: Jalili
authors.forename: Ali
authors.initials: A
authors.lastname: Moeini
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D057225:Data Mining; D030562:Databases, Protein; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010641:Phenotype; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D040901:Proteomics; D064210:Secondary Metabolism; D015398:Signal Transduction; D018709:Statistics, Nonparametric
chemical_list: D011506:Proteins
keywords: Dose–response relationship; Molecular targeted therapy; Neuronal plasticity; Signaling pathway