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Causal reasoning on Boolean control networks based on abduction: theory and application to cancer drug discovery

Abstract : Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of disease-induced molecular perturbations and subsequent development of efficient therapies are challenged by the complexity of the genotype-phenotype relationship. Accordingly, a key issue is to develop frameworks relating molecular perturbations and drug effects to their consequences on cellular phenotypes. Such framework would aim at identifying the sets of causal molecular factors leading to phenotypic reprogramming. In this article, we propose a theoretical framework, called Boolean Control Networks, where disease-induced molecular perturbations and drug actions are seen as topological perturbations/actions on molecular networks leading to cell phenotype reprogramming. We present a new method using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. Then, we compare different implementations of the algorithm and finally, show a proof-of-concept of the approach on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and their synthetic lethal drug target partner.
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Submitted on : Wednesday, August 7, 2019 - 2:31:30 PM
Last modification on : Saturday, May 1, 2021 - 3:50:23 AM
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Célia Biane, Franck Delaplace. Causal reasoning on Boolean control networks based on abduction: theory and application to cancer drug discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16 (5), pp.1574--1585. ⟨10.1109/TCBB.2018.2889102⟩. ⟨hal-02264665⟩



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