Abstract
Toxicology aims at studying the adverse effects of exogenous chemicals on organisms. As these effects mainly concern metabolic pathways, reasoning about toxicity would involve metabolism modeling approaches. Usually, metabolic network models approaches are rule-based and describe chemical reactions, indirectly depicting equilibria as results of competing rule kinetics. By altering these kinetics, an exogenous compound can shift the system equilibria and induce toxicity. As equilibria are kept implicit, the identification of possible toxicity pathways is hindered as they require a fine understanding of chemical reactions dynamics to infer possible equilibria disruptions. Paradoxically, the toxicity pathways are based on a succession of very abstract (coarse grained) events. To reduce this mismatch, we propose a more abstract framework making equilibria first-class citizens. Our rules describe qualitative equilibrium changes and the chaining of rules is controlled by constraints expressed in extended temporal logic. This higher abstraction level fosters the detection of toxicity pathways, as we will show through an example of endocrine disruption of the thyroid hormone system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barter, R.A., Klaassen, C.D.: Reduction of thyroid hormone levels and alteration of thyroid function by four representative UDP-glucuronosyltransferase inducers in rats. Toxicol. Appl. Pharmacol. 128(1), 9–17 (1994)
Bernot, G., Comet, J.P., Richard, A., Guespin, J.: Application of formal methods to biological regulatory networks: extending Thomas’ asynchronous logical approach with temporal logic. J. Theor. Biol. 229(3), 339–347 (2004)
Chabrier-Rivier, N., Fages, F., Soliman, S.: The biochemical abstract machine BIOCHAM. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS, vol. 3082, pp. 172–191. Springer, Heidelberg (2005). doi:10.1007/978-3-540-25974-9_14
Chaki, S., Clarke, E.M., Ouaknine, J., Sharygina, N., Sinha, N.: State/Event-based software model checking. In: Boiten, E.A., Derrick, J., Smith, G. (eds.) IFM 2004. LNCS, vol. 2999, pp. 128–147. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24756-2_8
Danos, V., Feret, J., Fontana, W., Harmer, R., Hayman, J., Krivine, J., Thompson-Walsh, C., Winskel, G.: Graphs, rewriting and pathway reconstruction for rule-based models. In: LIPIcs-Leibniz International Proceedings in Informatics, vol 18. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2012)
Dentice, M., Salvatore, D.: Local impact of thyroid hormone inactivation deiodinases: the balance of thyroid hormone. J. Endocrinol. 209(3), 273–282 (2011)
Fages, F., Soliman, S.: Formal cell biology in biocham. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 54–80. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68894-5_3
Isler, H., Leblond, C., Axelrad, A.: Influence of age and of iodine intake on the production of thyroid tumors in the rat 2. J. Natl. Cancer Inst. 21(6), 1065–1081 (1958)
Kavlock, R.J., Ankley, G., Blancato, J., Breen, M., Conolly, R., Dix, D., Houck, K., Hubal, E., Judson, R., Rabinowitz, J., et al.: Computational toxicologya state of the science mini review. Toxicol. Sci. 103(1), 14–27 (2008)
Kopp, P.: Thyroid Hormone Synthesis. Werner and Ingbars The Thyroid. A Fundamental and Clinical Text, 10th edn., pp. 48–74 (2012)
Maia, A.L., Goemann, I.M., Meyer, E.L.S., Wajner, S.M.: Type 1 iodothyronine deiodinase in human physiology and disease deiodinases: the balance of thyroid hormone. J. Endocrinol. 209(3), 283–297 (2011)
Marians, R., Ng, L., Blair, H., Unger, P., Graves, P., Davies, T.: Defining thyrotropin-dependent and-independent steps of thyroid hormone synthesis by using thyrotropin receptor-null mice. Proc. Natl. Acad. Sci. 99(24), 15776–15781 (2002)
McClain, R.M.: Thyroid gland neoplasia: non-genotoxic mechanisms. Toxicol. Lett. 64, 397–408 (1992)
Moeller, L.C., Cao, X., Dumitrescu, A.M., Seo, H., Refetoff, S.: Thyroid hormone mediated changes in gene expression can be initiated by cytosolic action of the thyroid hormone receptor beta through the phosphatidylinositol 3-kinase pathway. Nucl. Recept. Sig. 4, e020 (2006)
Nagasaki, M., Onami, S., Miyano, S., Kitano, H.: Bio-calculus: Its concept and molecular interaction. Genome Inf. 10, 133–143 (1999)
Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: Bioambients: an abstraction for biological compartments. Theoret. Comput. Sci. 325(1), 141–167 (2004)
Rosene, M.L., Wittmann, G., Arrojo e Drigo, R., Singru, P.S., Lechan, R.M., Bianco, A.C.: Inhibition of the type 2 iodothyronine deiodinase underlies the elevated plasma TSH associated with amiodarone treatment. Endocrinology 151(12), 5961–5970 (2010)
Snoussi, E.H.: Qualitative dynamics of piecewise-linear differential equations: a discrete mapping approach. Dyn. Stab. Syst. 4(3–4), 565–583 (1989)
Talcott, C.: Pathway logic. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 21–53. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68894-5_2
Thomas, R.: Regulatory networks seen as asynchronous automata: a logical description. J. Theor. Biol. 153(1), 1–23 (1991)
Visser, T.J.: Role of sulfation in thyroid hormone metabolism. Chem. Biol. Interact. 92(1–3), 293–303 (1994)
Williams, G.R., Bassett, J.D.: Local control of thyroid hormone action: role of type 2 deiodinase deiodinases: the balance of thyroid hormone. J. Endocrinol. 209(3), 261–272 (2011)
Zoeller, R.T., Tan, S.W., Tyl, R.W.: General background on the hypothalamic-pituitary-thyroid (HPT) axis. Crit. Rev. Toxicol. 37(1–2), 11–53 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Miraglio, B., Bernot, G., Comet, JP., Faverney, C.Rd. (2017). Detecting Toxicity Pathways with a Formal Framework Based on Equilibrium Changes. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-67471-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67470-4
Online ISBN: 978-3-319-67471-1
eBook Packages: Computer ScienceComputer Science (R0)