Reasoning over large-scale and uncertain systems in biology


Dyliss team, IRISA lab., Rennes, F


   Systems modeled in the context of molecular and cellular biology are highly difficult to identify in unique way. In this context, we will describe how several approaches based on reasoning allow the systems to be identified, validated, improved and finally studied in the framework of uncertainty. To that goal, we rely on Answer Set Programming, a paradigm of logical programming. This paradigm is associated to studies of dynamical systems from one side, and to querying systems from another side, in order to extract relevant properties of a system under study. We will illustrate this approach on signaling networks and metabolic networks.