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Querying new information from knowledge sources, in general, and publishedliterature, in particular, aims to provide precise and quick answers toquestions raised about a system under study. In this paper, we presentACCORDION (Automated Clustering Conditional On Relating Data of Interactions tOa Network), a novel tool and a methodology to enable efficient answering ofbiological questions by automatically assembling new, or expanding existingmodels using published literature. Our approach integrates informationextraction and clustering with simulation and formal analysis to allow for anautomated iterative process that includes assembling, testing and selecting themost relevant models, given a set of desired system properties. We applied ourmethodology to a model of the circuitry that con-trols T cell differentiation.To evaluate our approach, we compare the model that we obtained, using ourautomated model extension approach, with the previously published manuallyextended T cell differentiation model. Besides demonstrating automated andrapid reconstruction of a model that was previously built manually, ACCORDIONcan assemble multiple models that satisfy desired properties. As such, itreplaces large number of tedious or even imprac-tical manual experiments andguides alternative hypotheses and interventions in biological systems.

ACCORDION: Clustering and Selecting Relevant Data for Guided Network Extension and Query Answering