Guided assembly of cellular network models from knowledge in literature

TL;DR

This paper introduces an automated method for assembling cellular network models by extracting and organizing relevant literature information into a collaboration graph, significantly speeding up model extension processes.

Contribution

The novel approach leverages literature-based event extraction and graph analysis to efficiently suggest model extensions, reducing manual effort and time.

Findings

Achieved an average recall of 82% in identifying relevant events.

Effectively applied to models of T cell differentiation, T cell LGL, and pancreatic cancer.

Demonstrated the method's reliability across different biological models.

Abstract

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%.