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.
|Translated title of the contribution||Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data|
|Title of host publication||19th International Conference on Inductive Logic Programming|
|Publication status||Published - 2010|
Bibliographical noteOther page information: 194-201
Conference Proceedings/Title of Journal: 19th International Conference on Inductive Logic Programming
Other identifier: 2001245