Automating the Development of Metabolic Network Models

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