IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-0-387-88630-5_7.html
   My bibliography  Save this book chapter

A Genetic Classifier Account for the Regulation of Expression

In: Computational Neuroscience

Author

Listed:
  • Tsvi Achler

    (University of Illinois Urbana-Champaign)

  • Eyal Amir

    (University of Illinois Urbana-Champaign)

Abstract

This work is motivated by our model of neuroscience processing which incorporates large numbers of reentrant top-down feedback regulation connections. Such regulation is fundamental and can be found throughout biology. The purpose of this chapter is to broaden this model's application. Genes perform important life functions, responsible for virtually every organic molecule that organisms produce. The genes must closely regulate the amount of their products, because too little or too much production may be deleterious for the organism. Furthermore, they must respond efficiently and in unison to the environments that the organism faces. Networks that are closely regulated can behave as robust classifiers which can recognize and respond to their environment. Using simple examples we demonstrate that such networks perform dynamic classification, determining the most efficient set of genes needed to replace consumed products.

Suggested Citation

  • Tsvi Achler & Eyal Amir, 2010. "A Genetic Classifier Account for the Regulation of Expression," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Panos M. Pardalos & Petros Xanthopoulos (ed.), Computational Neuroscience, chapter 0, pages 113-123, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88630-5_7
    DOI: 10.1007/978-0-387-88630-5_7
    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.

    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:spr:spochp:978-0-387-88630-5_7. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.