title: Elementary signaling modes predict the essentiality of signal transduction network components.
abstract: BACKGROUND
  Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction.
  
  
  RESULTS
  In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations.
  
  
  CONCLUSIONS
  This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM.
authors.affiliation: Department of Physics, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Rui-Sheng
authors.initials: RS
authors.lastname: Wang
authors.forename: Réka
authors.initials: R
authors.lastname: Albert
mesh_terms: D000040:Abscisic Acid / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D018515:Plant Leaves / Q000378:metabolism; D011948:Receptors, Antigen, T-Cell / Q000378:metabolism; D015398:Signal Transduction / Q000276:immunology* / Q000502:physiology*; D049490:Systems Biology / Q000379:methods*
chemical_list: D011948:Receptors, Antigen, T-Cell; D000040:Abscisic Acid