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There is significant interest in using existing repositories of biologicalentities, relationships, and models to automate biological model assembly andextension. Current methods aggregate human-curated biological information intoexecutable, simulatable models, but these models do not resemble human curatedmodels and do not recapitulate experimental results. Here, we outline theprocess of automated model assembly and extension, while demonstrating it onboth synthetic models and human-curated models of biological signalingnetworks. We begin with an iterative, greedy, and combinatoric approach toautomated assembly and demonstrate the key difficulties inherent to contextlessassembly. We publicly release the software used in this paper to enable furtherexploration of this problem.

Automating Knowledge-Driven Model Recommendation: Methodology, Evaluation, and Key Challenges