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Bioinformatic and modelling approaches for a system-level understanding of oxidative stress toxicity

Abstract : New understanding of biology shows more and more that the mechanisms that underlie toxicity are complex and involve multiple biological processes and pathways. Adverse outcome pathways (AOPs) and systems biology (SB) can be appropriate tools for studying toxicology at this level of complexity. This PhD thesis focuses on the elaboration of a SB model of the role of the Nrf2 pathway in the control of oxidative stress. The model’s calibration with experimental data (obtained with RPTEC/TERT1 renal cells exposed to various doses of potassium bromate) comprised several rounds of hypotheses stating/verification, through which new reactions were progressively added to the model. Some of these new hypotheses (e.g., direct action of potassium bromate on DCF, bleaching of DCF with time, etc.) could be confirmed by future experiments. Considered in a wider framework, this SB model was then evaluated and compared to two other computational models (i.e., an empirical dose-response statistical model and a dynamic Bayesian model) for the quantification of a ‘chronic kidney disease’ AOP. All parameter calibrations were done by MCMC simulations with the GNU MCSim software with a quantification of uncertainties associated with predictions. Even though the SB model was indeed complex to conceive, it offers insight in biology that the other approaches could not afford. In addition, using multiple toxicogenomic databases; interactions and cross-talks of the Nrf2 pathway with two other toxicity pathways (i.e., AhR and ATF4) were examined. The results of this last analysis suggest adding new AhR contribution to the control of some of the Nrf2 genes in our SB model (e.g., HMOX1, SRXN1 and GCLM), and integrating in it description of the ATF4 pathway (partially at least). Despites their complexity, precise SB models are precious investments for future developments in predictive toxicology.
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https://tel.archives-ouvertes.fr/tel-02088169
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Submitted on : Tuesday, April 2, 2019 - 4:06:10 PM
Last modification on : Wednesday, April 3, 2019 - 9:31:05 AM
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Elias Zgheib. Bioinformatic and modelling approaches for a system-level understanding of oxidative stress toxicity. Quantitative Methods [q-bio.QM]. Université de Technologie de Compiègne, 2018. English. ⟨NNT : 2018COMP2464⟩. ⟨tel-02088169⟩

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