title: Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.
abstract: Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.
authors.affiliation: Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, New York, United States of America.
authors.forename: Yishai
authors.initials: Y
authors.lastname: Shimoni
authors.forename: Marc Y
authors.initials: MY
authors.lastname: Fink
authors.forename: Soon-gang
authors.initials: SG
authors.lastname: Choi
authors.forename: Stuart C
authors.initials: SC
authors.lastname: Sealfon
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004815:Epidermal Growth Factor / Q000235:genetics / Q000378:metabolism; D015870:Gene Expression; D053263:Gene Regulatory Networks; D048031:JNK Mitogen-Activated Protein Kinases / Q000235:genetics / Q000378:metabolism; D051379:Mice; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D012333:RNA, Messenger; D004815:Epidermal Growth Factor; D048031:JNK Mitogen-Activated Protein Kinases