IDEAS home Printed from
   My bibliography  Save this paper

Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data


  • Andreas Wagner


Large scale gene perturbation experiments generate information about the number of genes whose activity is directly or indirectly affected by a gene perturbation. From this information, one can numerically estimate coarse structural network features such as the total number of direct regulatory interactions and the number of isolated subnetworks in a transcriptional regulation network. Applied to the results of a large-scale gene knockout experiment in the yeast Saccharomyces cerevisiae, the results suggests that the yeast transcriptional regulatory network is very sparse, containing no more direct regulatory interactions than genes. The network comprises more than one hundred independent sub-networks.

Suggested Citation

  • Andreas Wagner, 2001. "Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data," Working Papers 01-09-051, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:01-09-051

    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

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

    Cited by:

    1. Romualdo Pastor-Satorras & Eric Smith & Ricard V. Solé, 2002. "Evolving Protein Interaction Networks through Gene Duplication," Working Papers 02-02-008, Santa Fe Institute.

    More about this item


    Genomics; reverse engineering; microarrays;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:01-09-051. 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: .

    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.