skip to main content
10.1145/2506583.2506695acmconferencesArticle/Chapter ViewAbstractPublication PagesbcbConference Proceedingsconference-collections
tutorial

Identifying Pathway Proteins in Networks using Convergence

Published: 22 September 2013 Publication History

Abstract

One of the key goals of systems biology concerns the analysis of experimental biological data available to the scientific public. New technologies are rapidly developed to observe and report whole-scale biological phenomena; however, few methods exist with the ability to produce specific, testable hypotheses from this noisy 'big' data. In this work, we propose an approach that combines the power of data-driven network theory along with knowledge-based ontology to tackle this problem. Network models are especially powerful due to their ability to display elements of interest and their relationships as internetwork structures. Additionally, ontological data actually supplements the confidence of relationships within the model without clouding critical structure identification. As such, we postulate that given a (gene/protein) marker set of interest, we can systematically identify the core of their interactions (if they are indeed working together toward a biological function), via elimination of original markers and addition of additional necessary markers. This concept, which we refer to as "convergence," harnesses the idea of "guilt-by-association" and recursion to identify whether a core of relationships exists between markers. In this study, we test graph theoretic concepts such as shortest-path, k-Nearest-Neighbor and clustering) to identify cores iteratively in data- and knowledge-based networks in the canonical yeast Pheromone Mating Response pathway. Additionally, we provide results for convergence application in virus infection, hearing loss, and Parkinson's disease. Our results indicate that if a marker set has common discrete function, this approach is able to identify that function, its interacting markers, and any new elements necessary to complete the structural core of that function. The result below find that the shortest path function is the best approach of those used, finding small target sets that contain a majority or all of the markers in the gold standard pathway. The power of this approach lies in its ability to be used in investigative studies to inform decisions concerning target selection.

References

[1]
Gu Z, Wang J. CePa: An R package for finding significant pathways weighted by multiple network centralities. Bioinformatics. 2013.
[2]
Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41--42.
[3]
Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559-2105-9-559.
[4]
Sali A, Glaeser R, Earnest T, Baumeister W. From words to literature in structural proteomics. Nature. 2003; 422(6928):216--225.
[5]
Sarac OS, Pancaldi V, Bahler J, Beyer A. Topology of functional networks predicts physical binding of proteins. Bioinformatics. 2012;28(16):2137--2145.
[6]
Qi Y, Balem F, Faloutsos C, Klein-Seetharaman J, Bar-Joseph Z. Protein complex identification by supervised graph local clustering. Bioinformatics. 2008;24(13):i250--8.
[7]
Rhrissorrakrai K, Gunsalus KC. MINE: Module identification in networks. BMC Bioinformatics. 2011;12:192-2105-12-192.
[8]
Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.
[9]
Managbanag JR, Witten TM, Bonchev D, et al. Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS One. 2008;3(11):e3802.
[10]
Gustin MC, Albertyn J, Alexander M, Davenport K. MAP kinase pathways in the yeast saccharomyces cerevisiae. Microbiol Mol Biol Rev. 1998;62(4):1264--1300.
[11]
Bardwell L. A walk-through of the yeast mating pheromone response pathway. Peptides. 2005;26(2):339--350.
[12]
Hemsley PA, Grierson CS. The ankyrin repeats and DHHC S-acyl transferase domain of AKR1 act independently to regulate switching from vegetative to mating states in yeast. PLoS One. 2011;6(12):e28799.
[13]
Pryciak PM, Hartwell LH. AKR1 encodes a candidate effector of the G beta gamma complex in the saccharomyces cerevisiae pheromone response pathway and contributes to control of both cell shape and signal transduction. Mol Cell Biol. 1996;16(6):2614--2626.
[14]
Cullen PJ, Sprague GF, Jr. The Glc7p-interacting protein Bud14p attenuates polarized growth, pheromone response, and filamentous growth in saccharomyces cerevisiae. Eukaryot Cell. 2002;1(6):884--894.
[15]
Li X, Wu M, Kwoh CK, Ng SK. Computational approaches for detecting protein complexes from protein interaction networks: A survey. BMC Genomics. 2010;11 Suppl 1:S3-2164-11-S1-S3.
  1. Identifying Pathway Proteins in Networks using Convergence

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
    September 2013
    987 pages
    ISBN:9781450324342
    DOI:10.1145/2506583
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 September 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Graph theory
    2. biological networks
    3. convergence
    4. ontology

    Qualifiers

    • Tutorial
    • Research
    • Refereed limited

    Conference

    BCB'13
    Sponsor:
    BCB'13: ACM-BCB2013
    September 22 - 25, 2013
    Wshington DC, USA

    Acceptance Rates

    BCB'13 Paper Acceptance Rate 43 of 148 submissions, 29%;
    Overall Acceptance Rate 254 of 885 submissions, 29%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 69
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media