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MOSBIE: a tool for comparison and analysis of rule-based biochemical models

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journal contribution
posted on 16.05.2016 by J.E. Wenskovitch Jr, L.A. Harris, J-J. Tapia, J.R. Faeder, G.E. Marai
Background: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. Results: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. Conclusions: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.


This work has been supported by grant NSF-IIS-0952720, the Pitt Clinical Translational Science Institute (Fellows Program) 5UL1RR024153-05, and NIH/NIGMS grant P41GM103712. Many thanks to Tim Luciani and Adam Smith for help in testing and debugging, and to the other members of the Marai VisLab and Faeder Lab for their feedback and useful discussions.


Publisher Statement

This is a copy of an article published in the BMC Bioinformatics © 2014 BioMed Central Publications.


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