IDEAS home Printed from https://ideas.repec.org/p/wop/safiwp/02-02-006.html
   My bibliography  Save this paper

Mutational Robustness and Asymmetric Functional Specialization of Duplicate Genes

Author

Listed:
  • Andreas Wagner

Abstract

Most duplicate genes are eliminated from a genome shortly after duplication, but those that remain are an important source of biochemical diversity. Much of their diversification arises via functional ÒspecializationÓ, loss of some functions of the duplicates remaining in the genome. I here present evidence from genome-scale protein-protein interaction data, microarray expression data, and large-scale gene knockout data that this diversification is often asymmetrical: one duplicate usually shows significantly more molecular or genetic interactions than the other. I propose a model that can explain this divergence pattern if duplicate gene pairs are less likely to suffer deleterious mutations when having diverged asymmetrically. The data may provide the first evidence that natural selection has increased mutational robustness in genetic networks.

Suggested Citation

  • Andreas Wagner, 2002. "Mutational Robustness and Asymmetric Functional Specialization of Duplicate Genes," Working Papers 02-02-006, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:02-02-006
    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. Andreas Wagner, 2001. "The Yeast Protein Interaction Network Evolves Rapidly and Contains Few Redundant Duplicate Genes," Working Papers 01-04-022, Santa Fe Institute.
    2. Aaron E. Hirsh & Hunter B. Fraser, 2001. "Protein dispensability and rate of evolution," Nature, Nature, vol. 411(6841), pages 1046-1049, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Oren Ish-Am & David M Kristensen & Eytan Ruppin, 2015. "Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    8. Eleanor R Brush & David C Krakauer & Jessica C Flack, 2013. "A Family of Algorithms for Computing Consensus about Node State from Network Data," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-17, July.
    9. 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.

    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:02-02-006. See general information about how to correct material in RePEc.

    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 bibliographic 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.