Introduction and Overview. The Potential of Metabonomics in Toxicology. Hormesis: A Key Concept in Toxicology. Chemical Risk Assessment. Toxicology of Nanomaterials. Drug Safety Toxicology.
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These data are obtained by applying the suitable statistical models at the individual gene level and extend over the full transcriptome, in line with the first aspect of the holistic perspective of systems biology discussed above. A suitably organized collection of causal networks covering the essential biological mechanisms of the test system response to the applied exposure treatment.
Unlike other types of networks, causal biological networks contain nodes that not only describe molecular concentrations but also represent functions such transcriptional, enzymatic, or kinase activities.
3S – Systematic, Systemic, and Systems Biology and Toxicology
The network edges encode causal i. The underlying biological knowledge in these networks has been manually extracted from the scientific literature and encoded in the biological expression language BEL , an ontology developed specifically for causal biological networks. The current version of the causal biological network collection is publicly available on the causal biological network CBN database website [ 26 , 27 ].
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The recent developments around the causal networks are discussed in Section 3. Transcriptional footprints are transcript abundance nodes that are connected to the causal network nodes via signed directed edges, similar to the ones in the causal networks. In our assessment applications, we licensed the Selventa Knowledgebase to get a good coverage of the nodes of the causal network collection in terms of transcriptional footprints [ 28 ].
Essentially, this consists of performing an edge-based, weighted average of the differential gene expressions attached to the transcriptional footprint nodes [ 23 ]. This property means that the edge-based relative sign between any two nodes must be unambiguous i. As most networks do not satisfy this condition e. Calculation of the perturbations for all network nodes based on a constraint optimization problem. Calculation of the NPAs using an edge-based summation. The summed values are the squared edge sign-corrected mean of the corresponding node smoothed perturbation values.
As this value is always positive, it is important to examine the node-level perturbation values to determine whether the underlying biological mechanism is activated or inhibited as a consequence of the exposure treatment. Calculation of three accompanying statistics to decide whether the obtained NPA value represents a true or a false positive.
The other two statistics test the relevance of the biological mechanism s encoded in the network by randomly reshuffling the network edges or the transcriptional footprints. This yields two null distributions for the network-level perturbation values. Significant network perturbations correspond to the cases where all three statistical tests are successful. The calculations of the network perturbation amplitudes NPAs and biological impact factor BIF in a bottom-up representation.
The six layers correspond to the six steps 1—6 explained in the main text.