Computational Systems Toxicology

Softcover Nachdruck of the original 1. Auflage 2015
ISBN: 978-1-4939-5003-4
Verlag: Springer

Buch, Englisch, 430 Seiten, Paperback, Format (B × H): 178 mm x 254 mm, Gewicht: 1127 g

Reihe: Methods in Pharmacology and Toxicology

This detailed volume explores key state-of-the-art computational applications that are crucial in Systems Toxicology. The recent technological developments in experimental biology and multi-omics measurements that enable Systems Biology and Systems Toxicology can only be fully leveraged by the application of a broad range of computational approaches ranging from data management to mathematical modeling. Taking this into account, chapters in this book cover data management and processing, data analysis, biological network building and analysis, as well as the application of computational methods to toxicological assessment.

Written for the Methods in Pharmacology and Toxicology series, Computational Systems Toxicology includes the kind of key practical advice that will aid readers in furthering our knowledge of toxic substances and reactions to them.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Peitsch, Manuel C.

Hoeng, Julia

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie

Inhaltsverzeichnis

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.