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Visual support for structural and functional analysis of complex signaling networks in ProMoT

MPS-Authors
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Mirschel,  S.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Saez-Rodriguez,  J.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Ginkel,  Martin
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Gilles,  E. D.
Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Citation

Mirschel, S., Saez-Rodriguez, J., Ginkel, M., & Gilles, E. D. (2008). Visual support for structural and functional analysis of complex signaling networks in ProMoT. Poster presented at 9th International Conference on Systems Biology (ICSB08), Gothenburg, Sweden.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9505-3
Abstract
Objective: In recent years signaling aspects of biological systems become more and more popular. Based on massive data sets, signaling networks are fast growing in terms of size and complexity. Thus, the structural and functional analysis of large signaling networks using a logical (Boolean) model formalism is a valuable approach. In contrast to more detailed, quantitative descriptions, the simplicity of this approach allows to handle considerable systems and couple models to large sets of data. However, the interpretation of such networks and their analysis may not be trivial as they rely on the properties of complex networks. Therefore, adequate visual presentations would be of great value. Results: We present an approach aimed to provide intuitive and flexible visual representations of analysis data. The results are directly mapped to and visually encoded in the analyzed network. For example, it is feasible to visually group identical or similar data (e.g. to depict possible correlations between proteins or functional groups), to emphazise non-trivial or unexpected data (e.g. activation of a certain protein that was not expected) and to de-emphazise data that is not relevant in the context of a specific analysis (e.g. hide proteins that are not involved in a certain pathway). Another interesting feature is the simultaneous presentation of heterogeneous analysis results within a single illustration by encoding them using different visual properties (e.g.,for a certain protein, the initial value and the value obtained from the analysis are mapped to the node color and the node label, respectively). Conclusions: The presented approach supports the analysis of complex signaling phenomena and the integration of interrelated analysis results using visual methods. These methods are part of a visual environment implemented in the modeling tool ProMoT, where models can also be set up and exported for analysis (Saez-Rodriguez, Mirschel et al, BMC Bioinf, 7:506, 2006). ProMoT is freely available for download at http://www.mpi-magdeburg.mpg.de/de/projects/promot.