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Biological Network Inference and Analysis Using SEBINI and CABIN

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Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 541))

Abstract

Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level, and toward intelligent, efficient, and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein abundance, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is an exploratory data analysis software that enables integration and analysis of protein–protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in a public database, along with the confidence held in each edge (if available), can be fed into CABIN as one “evidence network,” using the Cytoscape SIF file format. Using CABIN, one may increase the confidence in individual edges in a network inferred by an algorithm in SEBINI, as well as extend such a network by combining it with species-specific or generic information, e.g., known protein–protein interactions or target genes identified for known transcription factors. Thus, the combined SEBINI–CABIN toolkit aids in the more accurate reconstruction of biological networks, with less effort, in less time.

A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/SEBINI/RootServlet. Source code and PostgreSQL database schema are available under open source license. Contact: ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. CABIN can be downloaded from http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

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Acknowledgments

The research described in this paper was conducted under the Laboratory Directed Research and Development Program at the Pacific Northwest National Laboratory (PNNL), a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy, under Contract DE-AC06-76RL01830. Also, work for SEBINI has been supported by PNNL’s William R. Wiley Environmental Molecular Science Laboratory (EMSL) and the EMSL Grand Challenge in Membrane Biology project, and by the joint ORNL / PNNL collaboration for the Genomes to Life Center for Molecular and Cellular Biology, project # 43930, US Department of Energy.

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  1. Computational Biology and Bioinformatics Group, Computational and Informational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA

    Ronald Taylor & Mudita Singhal

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  1. Ronald Taylor
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  2. Mudita Singhal
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    © 2009 Humana Press, a part of Springer Science+Business Media, LLC

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    Taylor, R., Singhal, M. (2009). Biological Network Inference and Analysis Using SEBINI and CABIN. In: Ireton, R., Montgomery, K., Bumgarner, R., Samudrala, R., McDermott, J. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 541. Humana Press. https://doi.org/10.1007/978-1-59745-243-4_24

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    • DOI: https://doi.org/10.1007/978-1-59745-243-4_24

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    • Print ISBN: 978-1-58829-905-5

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