IDEAS home Printed from https://ideas.repec.org/p/wop/safiwp/01-04-022.html
   My bibliography  Save this paper

The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes

Author

Listed:
  • Andreas Wagner

Abstract

The structure and evolution of the protein interaction network of the yeast Saccharomyces cerevisiae is analyzed. The network is viewed as a graph whose nodes correspond to proteins. Two proteins are connected by an edge if they interact. The network resembles a random graph, in that it consists of many small subnets, groups of proteins that interact with each other but do not interact with any other protein, and one large connected subnet comprising more than half of all interacting proteins. The number of interactions per protein appears to follow a power-law distribution. Within approximately 200 million years after a duplication, the products of duplicate genes become almost equally likely to (i) have common protein interaction partners, and (ii) be part of the same sub-network, as two proteins chosen at random from within the network. This indicates that the persistence of redundant interaction partners is the exception rather than the rule. After gene duplication, the likelihood that an interaction gets lost exceeds 2.2x10-3 per million years. New interactions are estimated to evolve at rate that is approximately three orders of magnitude smaller. Every 300 million years, as many as half of all interactions may become replaced by new interactions.

Suggested Citation

  • Andreas Wagner, 2001. "The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes," Working Papers 01-04-022, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:01-04-022
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Gernot Grabher & Walter W. Powell (ed.), 2004. "Networks," Books, Edward Elgar Publishing, volume 0, number 2771.
    2. Andreas Wagner & David Fell, 2000. "The Small World Inside Large Metabolic Networks," Working Papers 00-07-041, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cárdenas, J.P. & Mouronte, M.L. & Benito, R.M. & Losada, J.C., 2010. "Compatibility as underlying mechanism behind the evolution of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1789-1798.
    2. Gao, Yuyang & Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Comparison of directed and weighted co-occurrence networks of six languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 579-589.
    3. Li, Wenyuan & Lin, Yongjing & Liu, Ying, 2007. "The structure of weighted small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 708-718.
    4. How to Reconstruct a Large Genetic Network from n Gene Perturbations in Fewer than n2 Easy Steps, 2001. "How to Reconstruct a Large Genetic Network from," Working Papers 01-09-047, Santa Fe Institute.
    5. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.
    6. Wylie, Dennis Cates, 2009. "Linked by loops: Network structure and switch integration in complex dynamical systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(9), pages 1946-1958.
    7. Sun, Lanfang & Jiang, Lu & Li, Menghui & He, Dacheng, 2006. "Statistical analysis of gene regulatory networks reconstructed from gene expression data of lung cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 663-671.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wop:safiwp:01-04-022. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel). General contact details of provider: http://edirc.repec.org/data/epstfus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.