Detecting Toxicity Pathways with a Formal Framework Based on Equilibrium Changes
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.
KeywordsDiscrete dynamic systems Rule-based modeling Temporal logic Computational toxicology
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