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Computational modeling is crucial for understanding and analyzing complexsystems. In biology, model creation is a human dependent task that requiresreading hundreds of papers and conducting wet lab experiments, which would takedays or months. To overcome this hurdle, we propose a novel automated method,that utilizes the knowledge published in literature to suggest model extensionsby selecting most relevant and useful information in few seconds. Inparticular, our novel approach organizes the events extracted from theliterature as a collaboration graph with additional metric that relies on theevent occurrence frequency in literature. Additionally, we show that commongraph centrality metrics vary in the assessment of the extracted events. Wehave demonstrated the reliability of the proposed method using three differentselected models, namely, T cell differentiation, T cell large granularlymphocyte, and pancreatic cancer cell. Our proposed method was able to findhigh percent of the desired new events with an average recall of 82%.

Guided assembly of cellular network models from knowledge in literature