Abstract
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.
Huginn is an open-source software, available at:
github.com/robaki/huginnCMSB2015.
All figures included in this paper are in public domain; files can be downloaded from:
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Notes
- 1.
From the Norse mythology – one of two ravens scouting the world for Odin.
- 2.
Tested using pair-wise comparison of improvement and then a binomial test.
- 3.
Tested using paired, one-tailed t-test.
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Acknowledgment
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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Rozanski, R., Bragaglia, S., Ray, O., King, R. (2015). Automating the Development of Metabolic Network Models. In: Roux, O., Bourdon, J. (eds) Computational Methods in Systems Biology. CMSB 2015. Lecture Notes in Computer Science(), vol 9308. Springer, Cham. https://doi.org/10.1007/978-3-319-23401-4_13
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