### 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.

Original language | English |
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Title of host publication | 19th International Conference on Inductive Logic Programming |

Publisher | Springer |

Publication status | Published - 2010 |

### Bibliographical note

Other page information: 194-201Conference Proceedings/Title of Journal: 19th International Conference on Inductive Logic Programming

Other identifier: 2001245

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## Cite this

Ray, O., Whelan, K., & King, R. (2010). Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data. In

*19th International Conference on Inductive Logic Programming*Springer. http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2001245