The toxicological effects of many stressors are mediated through unknown or incompletely characterized mechanisms of action The application of reverse engineering complex interaction networks from high dimensional miles data (gene protein metabolic signaling) can be used to overcome these limitations This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary gonadal endocrine axis in fathead minnows (FHM Pimephales promelas) Gene expression changes in FHM ovaries in response to seven different chemicals over different times doses and in vivo versus in vitro conditions were captured in a large data set of 868 arrays Potential AOPs of the antiandrogen flutamide were examined using two mutual information based methods to infer gene regulatory networks and potential AOPs Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment thus leading to the nodes associated with the adverse outcome Identification of candidate pathways allows for formation of testable hypotheses about key biological processes biomarkers or alternative endpoints that can be used to monitor an AOP Finally the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented Environ Toxicol Chem 2011,30 22-38 (C) 2010 SETAC
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