Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of diseaseinduced 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-ofconcept 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.

Abstract. A major challenge in cancer research is to determine the genetic mutations causing the cancerous phenotype of cells and conversely, the actions of drugs initiating programmed cell death in cancer cells. However, such a challenge is compounded by the complexity of the genotype-phenotype relationship and therefore, requires to relate the molecular effects of mutations and drugs to their consequences on cellular phenotypes. Discovering these complex relationships is at the root of new molecular drug targets discovery and cancer etiology investigation. In their elucidation, computational methods play a major role for the inference of the molecular causal actions from molecular and biological networks data analysis. In this article, we propose a theoretical framework where mutations and drug actions are seen as topological perturbations/actions on molecular networks inducing cell phenotype reprogramming. The framework is based on Boolean control networks where the topological network actions are modelled by control parameters. We present a new algorithm using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. The framework is validated on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and finding drug targets.