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

Wenskovitch, John E., Jr; Harris, Leonard A.; Tapia, Jose-Juan; Faeder, James R.; Marai, G. Elisabeta

doi:10.1186/1471-2105-15-316

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

Persistent URL

http://hdl.handle.net/10456/37046

Author(s)

Wenskovitch, John E., Jr

Harris, Leonard A.

Tapia, Jose-Juan

Faeder, James R.

Marai, G. Elisabeta

Date Issued

September 25, 2014

Abstract

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.

Journal

BMC Bioinformatics

Department

Computer Science

Citation

Wenskovitch et al.: MOSBIE: a tool for comparison and analysis of rule-based biochemical models. BMC Bioinformatics 2014 15:316

Publisher

BioMed Central Ltd

Version of Article

Published article

DOI

10.1186/1471-2105-15-316

ISSN

1471-2105

Rights

© 2014 Wenskovitch et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Subjects

Visualization

Visual computing

Rule-based modeling

Cell signaling

File(s)

Name

Wenskovitch 2014 BMC.pdf

Description

Published Article

Size

3.11 MB

Format

Adobe PDF

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