Bridging the gap between dynamical systems and their partial observation, computational models of biological processes aim at uncovering key mechanisms driving cellular dynamics, and ultimately predict their behaviour under unobserved conditions. The BNeDiction project aims at providing a general methodology for making predictions from data on systems structure and dynamics by the means of ensembles of Boolean networks, an unexplored direction. Based on recent advances on the symbolic and implicit formal characterization of the compatible models using logic programming, the key challenges relate to the sampling of ensemble of diverse models, and the evaluation and maximization of its predictive power. Overall, the project aspires at delivering a convincing methodology for assessing the adequacy of automated logical modelling from experimental data, a key and recurring question at the intersection of artificial intelligence and life sciences.
Principal investigator: Loïc Paulevé (CNRS, LaBRI, France)