title: Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks.
abstract: BACKGROUND
  Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.
  
  
  RESULTS
  We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes.
  
  
  CONCLUSIONS
  While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.
authors.affiliation: The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.
authors.forename: Ariel
authors.initials: A
authors.lastname: Feiglin
authors.forename: Adar
authors.initials: A
authors.lastname: Hacohen
authors.forename: Avital
authors.initials: A
authors.lastname: Sarusi
authors.forename: Jasmin
authors.initials: J
authors.lastname: Fisher
authors.forename: Ron
authors.initials: R
authors.lastname: Unger
authors.forename: Yanay
authors.initials: Y
authors.lastname: Ofran
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D066246:ErbB Receptors / Q000378:metabolism; D053263:Gene Regulatory Networks / Q000235:genetics*; D008954:Models, Biological; D010641:Phenotype; D019869:Phosphatidylinositol 3-Kinases / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology; D014409:Tumor Necrosis Factor-alpha / Q000378:metabolism
chemical_list: D014409:Tumor Necrosis Factor-alpha; D019869:Phosphatidylinositol 3-Kinases; C512478:EGFR protein, human; D066246:ErbB Receptors