Réseaux et Jeux pour la Médecine de Précision

Recent advances in omics technologies provide the leverage for the emergence of precision medicine aiming at personalizing therapy to patient. In this undertaking , computational methods play a central role for assisting physicians in their clinical decision making by combining data analysis and systems biology modelling. Complex diseases such as cancer or diabetes arise from the intricate interplay of various biological molecules. Therefore assessing drug eciency involves studying the eects of elementary perturbations caused by diseases on relevant biological networks. In this paper, we propose a computational framework called network action game applied to best drug selection problem combining game theory and discrete models of dynamics (Boolean networks). The decision-making is modeled using game theory that denes the process of drug selection among alternative possibilities, while Boolean networks are used for modeling the eects of the interplay between disease and drugs actions on the patient molecular system. The actions/strategies of disease and drugs are focused on arc alteration of the interactome. The eciency of this framework has been evaluated for drug prediction on a model of breast cancer signaling