A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks

This paper describes a new logic-based approach for representing and reasoning about metabolic networks.First it shows how biological pathways can be elegantly represented in a logic programming formalism able to model full chemical reactions with substrates and products in different cell compartments, and which are catalysed by iso-enzymes or enzyme-complexes that are subject to inhibitory feedbacks.Then it shows how a nonmonotonic reasoning system called XHAIL can be used as a practical method for learning and revising such metabolic networks from observational data. Preliminary results are described in which the approach is validated on a state-of-the-art model of aromatic amino acid biosynthesis.