  There is significant interest in using existing repositories of biological
entities, relationships, and models to automate biological model assembly and
extension. Current methods aggregate human-curated biological information into
executable, simulatable models, but these models do not resemble human curated
models and do not recapitulate experimental results. Here, we outline the
process of automated model assembly and extension, while demonstrating it on
both synthetic models and human-curated models of biological signaling
networks. We begin with an iterative, greedy, and combinatoric approach to
automated assembly and demonstrate the key difficulties inherent to contextless
assembly. We publicly release the software used in this paper to enable further
exploration of this problem.
