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Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data

Oliver Ray, Ken Whelan, Ross King, Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data. 19th International Conference on Inductive Logic Programming, pp. 194–201. July 2010. PDF, 375 Kbytes.

Abstract

This paper presents a nonmonotonic ILP approach for the automatic revision of metabolic networks through the logical analysis of experimental data. The method extends previous work in two respects: by suggesting revisions that involve both the addition and removal of in- formation; and by suggesting revisions that involve combinations of gene functions, enzyme inhibitions, and metabolic reactions. Our proposal is based on a new declarative model of metabolism expressed in a non- monotonic logic programming formalism. With respect to this model, a mixture of abductive and inductive inference is used to compute a set of minimal revisions needed to make a given network consistent with some observed data. In this way, we describe how a reasoning system called XHAIL was able to correctly revise a state-of-the-art metabolic pathway in the light of real-world experimental data acquired by an autonomous laboratory platform called the Robot Scientist.

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