  Computational modeling is crucial for understanding and analyzing complex
systems. In biology, model creation is a human dependent task that requires
reading hundreds of papers and conducting wet lab experiments, which would take
days or months. To overcome this hurdle, we propose a novel automated method,
that utilizes the knowledge published in literature to suggest model extensions
by selecting most relevant and useful information in few seconds. In
particular, our novel approach organizes the events extracted from the
literature as a collaboration graph with additional metric that relies on the
event occurrence frequency in literature. Additionally, we show that common
graph centrality metrics vary in the assessment of the extracted events. We
have demonstrated the reliability of the proposed method using three different
selected models, namely, T cell differentiation, T cell large granular
lymphocyte, and pancreatic cancer cell. Our proposed method was able to find
high percent of the desired new events with an average recall of 82%.
