Systems Toxicology (systox) is the use of computational methods from the field of Systems Biology to do Toxicology, the aim of which is to protect human health and the environment from the adverse effects of chemicals (see ‘what is toxicology?’).
The systems toxicology literature is growing rapidly, with several recent reviews setting out the aims and potential of the area e.g. Sturla et al 2014; Hartung et al. 2012. At the same time a number of detailed case studies are being published that show some of the challenges and opportunities of a systems toxicology approach. Examples include several studies of paracetamol (acetaminophen): Wang et al. 2013, Bhattacharya et al. 2012, Shintu et al. 2012, and Kienhuis et al. 2011.
reverse causal reasoning
A key technique emerging in Systems Toxicology is reverse causal reasoning (RCR, e.g. Catlett et al. 2013;Laifenfeld et al. 2014). RCR is a reverse engineering methodology in which mechanistic hypotheses are inferred from molecular profiling data. Prior knowledge is expressed as small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These networks represent biological cause-and-effect relationships compiled from the scientific literature, and are tested as hypotheses to explain observed differential measurements. RCR yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets (Catlett et al. 2013).
why systems toxicology?
Aims for systems toxicology include:
- achieving more predictive and accurate risk-assessment approaches.
- gaining a detailed mechanistic understanding of xenobiotic perturbation of biological systems and production of adverse outcomes.
- identifying and applying biomarkers for improved safety assessments.
Bhattacharya, S., Shoda, L. K., Zhang, Q., Woods, C. G., Howell, B. A., Siler, S. Q., Woodhead, J. L., Yang, Y., McMullen, P., Watkins, P. B., & Andersen, M. E. 2012, “Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches”, Front Physiol, vol. 3, p. 462. PubMed
Catlett, N. L., Bargnesi, A. J., Ungerer, S., Seagaran, T., Ladd, W., Elliston, K. O., & Pratt, D. 2013, “Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data”, BMC.Bioinformatics, vol. 14, p. 340. PubMed
Hartung, T., van, V. E., Jaworska, J., Bonilla, L., Skinner, N., & Thomas, R. 2012, “Systems toxicology”, ALTEX., vol. 29, no. 2, pp. 119-128. PubMed
Kienhuis, A. S., Bessems, J. G., Pennings, J. L., Driessen, M., Luijten, M., van Delft, J. H., Peijnenburg, A. A., & van, d., V 2011, “Application of toxicogenomics in hepatic systems toxicology for risk assessment: acetaminophen as a case study”, Toxicol.Appl.Pharmacol., vol. 250, no. 2, pp. 96-107. PubMed
Laifenfeld, D., Qiu, L., Swiss, R., Park, J., Macoritto, M., Will, Y., Younis, H. S., & Lawton, M. 2014, “Utilization of causal reasoning of hepatic gene expression in rats to identify molecular pathways of idiosyncratic drug-induced liver injury”, Toxicological Sciences, vol. 137, no. 1, pp. 234-248. PubMed
Shintu, L., Baudoin, R., Navratil, V., Prot, J. M., Pontoizeau, C., Defernez, M., Blaise, B. J., Domange, C., Pery, A. R., Toulhoat, P., Legallais, C., Brochot, C., Leclerc, E., & Dumas, M. E. 2012, “Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs”, Anal.Chem., vol. 84, no. 4, pp. 1840-1848. PubMed
Sturla SJ, Boobis AR, FitzGerald RE, Hoeng J, Kavlock RJ, Schirmer K, Whelan M, Wilks MF, Peitsch MC. Systems toxicology: from basic research to risk assessment. Chem Res Toxicol. 2014 Mar 17;27(3):314-29. doi: 10.1021/tx400410s. Epub 2014 Jan 21. PubMed
Wang, K., Yuan, Y., Li, H., Cho, J. H., Huang, D., Gray, L., Qin, S., & Galas, D. J. 2013, “The spectrum of circulating RNA: a window into systems toxicology”, Toxicological Sciences, vol. 132, no. 2, pp. 478-492. PubMed