Automatic revision of metabolic networks through logical analysis of experimental data

Oliver Ray, Ken Whelan, Ross King

    Research output: Book/ReportBook

    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 information; 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 nonmonotonic 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. © 2010 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    PublisherSpringer Nature
    Number of pages7
    ISBN (Print)364213839X, 9783642138393
    DOIs
    Publication statusPublished - 2010

    Publication series

    Name Lecture notes in computer science
    Volume5989

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