Automating the Development of Metabolic Network Models

  • Robert Rozanski
  • Stefano Bragaglia
  • Oliver Ray
  • Ross King
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9308)


Although substantial progress has been made in the automation of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on integrated systems that combine all of these aspects. This paper presents an active learning system, “Huginn”, that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We have validated our approach in a simulated environment using substantial test cases derived from a state-of-the-art model of yeast metabolism. We demonstrate that Huginn can not only improve metabolic models, but that it is able to both solve a wider range of biochemical problems than previous methods, and to utilise a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive Logic Programming for the first time.


Gene Deletion Metabolic Model Crucial Experiment Model Development Process Metabolic Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by an EPSRC-EU Doctoral Training Award and the Faculty Engineering and Physical Sciences of the University of Manchester.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Robert Rozanski
    • 1
  • Stefano Bragaglia
    • 2
  • Oliver Ray
    • 2
  • Ross King
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK
  2. 2.Department of Computer ScienceUniversity of BristolBristolUK

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