Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15986
Title: Adverse Outcome Pathway Networks II: Network Analytics.
Authors: Villeneuve, DL
Angrish, MM
Fortin, MC
Katsiadaki, I
Leonard, M
Margiotta-Casaluci, L
Munn, S
O'Brien, JM
Pollesch, NL
Smith, LC
Zhang, X
Knapen, D
Keywords: AOP network;Adverse outcome pathway;interactions;mixture toxicology;network topology;predictive toxicology;risk assessment
Issue Date: 2018
Citation: Environ Toxicol Chem, 2018
Abstract: Toxicological responses to stressors are more complex than the simple one biological perturbation to one adverse outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present paper introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using two example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses, or previously undefined emergent patterns of response, are introduced. Along with a companion article (Knapen et al. part I), these concepts set the stage for development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. Collectively, this work addresses one of the major themes identified through a SETAC Horizon Scanning effort focused on advancing the AOP framework. This article is protected by copyright. All rights reserved.
DOI: http://dx.doi.org/10.1002/etc.4124
ISSN: 1552-8618
Appears in Collections:Dept of Life Sciences Research Papers

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