About this Research Topic
Traditional toxicology focused on dose-dependently determining apical endpoints of toxicity. With the advent of toxicogenomics, efforts towards better understanding underlying molecular mechanisms has led to the development of the concept of Adverse Outcome Pathways, which are basically presented as a structural network of linearly related gene-gene interactions regulating key events for inducing apical toxic endpoints of interest. Impulse challenges from exposure of biological systems to toxic agents will however induce a cascade-type of events, presenting both adverse and adaptive processes thus requiring bioinformatics approaches and methods for complex dynamic data, generated not only across dose, but clearly also across time. Currently, time-resolved toxicogenomics data sets are increasingly being assembled in the course of large-scaled research projects, for instance devoted towards developing toxicogenomics-based predictive assays for evaluating chemical safety which are no longer animal-based. Consequently, this Research Topic will focus on emerging bioinformatics tools for identifying regulatory network motifs across dose and time, by exploiting the full extent of available toxicogenomics data sets, presenting a variety of unsupervised and supervised approaches applying statistical (e.g. co-expression models) and mathematical (e.g. differential equation models) methods, as well as data visualization approaches to capture the dynamic nature of the data by data mapping onto predefined biological pathways or more complex integrated networks.
Keywords: Dose-Time relationship, Network inference, Multiple omics, Data integration, Visualization
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