Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

Abstract

We describe how the genome-wide transcriptional profiling can be used in networkbased systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the "network perturbation amplitude" methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of highquality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the "quantitative adverse outcome pathway" concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the "Toxicity Testing in the 21st century" vision of the US National Research Council.

A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes

“…Systems biology relies on the principle that connections store more information than isolated pathways or gene sets [12]. We previously demonstrated that different transcriptomic profiles in response to nanoparticulate share similar regulatory mechanisms [6].…”

“…We previously described a systems toxicology approach for ENM grouping and prioritisation, demonstrating that including molecular alterations of biological systems after the exposure provides better molecular proxies of toxicity [11]. However, traditional approaches in mechanistic toxicogenomics do not explicitly require modelling the interactions between genes, and are based on the assessment of a linear representation of the response [12]. Established methods to characterise chemicals mechanism of action (MOA) using omics data, preprocess and filter data in order to select the most relevant alterations and characterise them functionally.…”