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Computational Systems Biology pp 101–143Cite as

Prediction and Integration of Regulatory and Protein–Protein Interactions

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

Abstract

Knowledge of transcriptional regulatory interactions (TRIs) is essential for exploring functional genomics and systems biology in any organism. While several results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast ( 1 ) and worm ( 2 ). Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a method for the integration of various data sets to predict TRIs for 54 organisms in the Bioverse ( 3 ). We describe how to compile and handle various formats and identifiers of data sets from different sources and how to predict TRIs using a homology-based approach, utilizing the compiled data sets. Integrated data sets include experimentally verified TRIs, binding sites of transcription factors, promoter sequences, protein subcellular localization, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein–protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological dynamics of an integrated network with gene expression under different conditions, essential for exploring functional genomics and systems biology.

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Acknowledgments

This work was supported in part by a Searle Scholar Award and NSF Grant DBI-0217241 to R.S., and the University of Washington’s Advanced Technology Initiative in Infectious Diseases. Also, it is supported in part by the National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science Technology & Development Agency (NSTDA), Thailand.

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Authors and Affiliations

  1. BIOTEC, Pathumthani, Thailand

    Duangdao Wichadakul

  2. Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA

    Jason McDermott

  3. Department of Microbiology, University of Washington, Seattle, WA, USA

    Ram Samudrala

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  1. Duangdao Wichadakul
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    Wichadakul, D., McDermott, J., Samudrala, R. (2009). Prediction and Integration of Regulatory and Protein–Protein Interactions. 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_6

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