—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 enzymecomplexes 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-ofthe-art model of Aromatic Amino Acid biosynthesis.
Oliver Ray, Ken E. Whelan, Ross D. King