By Julia Hoeng, Manuel C. Peitsch
This particular quantity explores key cutting-edge computational purposes which are the most important in structures Toxicology. the new technological advancements in experimental biology and multi-omics measurements that permit structures Biology and structures Toxicology can in simple terms be totally leveraged through the applying of a large diversity of computational techniques starting from facts administration to mathematical modeling. Taking this under consideration, chapters during this publication hide facts administration and processing, facts research, organic community development and research, in addition to the appliance of computational the right way to toxicological assessment.
Written for the Methods in Pharmacology and Toxicology sequence, Computational platforms Toxicology comprises the type of key functional recommendation that may relief readers in furthering our wisdom of poisonous elements and reactions to them.
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Wilbanks MS, Gust KA, Atwa S et al (2014) Validation of a genomics-based hypothetical adverse outcome pathway: 2,4-dinitrotoluene perturbs PPAR signaling thus impairing energy metabolism and exercise endurance. Toxicol Sci 141:44–58 36. Ankley GT, Villeneuve DL (2006) The fathead minnow in aquatic toxicology: past, present and future. Aquat Toxicol 78:91–102 37. Celander MC, Goldstone JV, Denslow ND et al (2011) Species extrapolation for the 21st century. Environ Toxicol Chem 30:52–63 38. Norris DO, Carr JA (2013) Vertebrate endocrinology, 5th edn.
Chemical categorization, prioritization, hazard identification, hazard characterization, and integrated approaches to testing and assessment . At the quantitative AOP stage, the KEs are supported by descriptions of how each KE can be measured and the accuracy and precision with which the measurements are made have been recorded. Additionally, a quantitative understanding exists of the magnitude and/or duration of change in the upstream KE needed to evoke some magnitude of change in the downstream KE.
For example, in the comparison of two metabolic networks of S. cerevisiae, the consensus on the reaction level was only around one third . A similar result was found for a comparison of two metabolic networks of A. thaliana , where only about one third of the reactions could be matched unambiguously. The overlap between the five human pathway databases that we have analyzed was about 3 % of the almost 7000 reactions they have combined . If only two networks were compared, the consensus varied from 11 % (EHMN versus Reactome) to 28 % (EHMN versus KEGG).
Computational Systems Toxicology by Julia Hoeng, Manuel C. Peitsch