Computational Systems Toxicology

<p>The Adverse Outcome Pathway: A Conceptual Framework to Support Toxicity Testing in the 21st Century.- Metabolic Pathway Databases: A Word of Caution.- Causal Biological Network Database: A Comprehensive Platform of Causal Biological Network Models Focused on the Pulmonary and Vascular Systems.- Data Sources for Signature Discovery in Toxicology.- Data Management and Processing in Toxicoinformatics: From Chemical Databases to Automatic Extraction of Unstructured Resources.- Using a Crowdsourcing Approach for Network Verification.- Toxicological Assessment Via Gene Network Analysis.- Weaving Knowledge into Biological Pathways in a Collaborative Manner.- Pathway Analysis and Machine Learning as Tools in Systems Toxicology.- Quantifying the Biological Impact of Active Substances Using Causal Network Models.- Analysis of Proteomic Data for Toxicological Applications.- Xenobiotic Metabolism Activation as a Biomarker of Cigarette Smoke Exposure Response.- High-Content Screening: Understanding and Managing Mechanistic Data to Better Predict Toxicity.- A Systems Toxicology Approach to Investigating the Cardiovascular Effects of Cigarette Smoke and Environmental Pollutants in ApoE-Deficient Mice.- Computational Data Integration in Toxicogenomics.- Aerosol Dosimetry Modeling Using Computational Fluid Dynamics.</p>