Ebook: [Article] Automating the Development of Metabolic Network Models

Abstract. Although substantial progress has been made in the automa-
tion 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 experi-
ment 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.