Automating the Development of Metabolic Network Models

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 more encompassing systems that combine all of these aspects. This paper presents an active learning system called Huginn that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We validate our approach in a simulated environment using test cases derived from a state-of-the-art model of yeast metabolism. We show that Huginn can not only improve metabolic models but that it is able to solve a wider range of biochemical problems than previous methods and use 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.