Detecting Toxicity Pathways with a Formal Framework Based on Equilibrium Changes

  • Benjamin Miraglio
  • Gilles Bernot
  • Jean-Paul Comet
  • Christine Risso-de Faverney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10545)

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.

Keywords

Discrete dynamic systems Rule-based modeling Temporal logic Computational toxicology 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Benjamin Miraglio
    • 1
  • Gilles Bernot
    • 1
  • Jean-Paul Comet
    • 1
  • Christine Risso-de Faverney
    • 2
  1. 1.Université Côte d’Azur, CNRS, I3SSophia AntipolisFrance
  2. 2.Université Côte d’Azur, CNRS, ECOMERSNiceFrance

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