• DocumentCode
    268845
  • Title

    Synthetic approaches to study transcriptional networks and noise in mammalian systems

  • Author

    Gregorio-Godoy, Paula ; Míguez, David G.

  • Author_Institution
    Dept. de Fis. de la Materia Condensada, Univ. Autonoma de Madrid, Madrid, Spain
  • Volume
    7
  • Issue
    1
  • fYear
    2013
  • fDate
    Feb-13
  • Firstpage
    11
  • Lastpage
    17
  • Abstract
    Synthetic biology aims to build new functional organisms and to rationally re-design existing ones by applying the engineering principle of modularity. Apart from building new life forms to perform technical applications, the approach of synthetic biology is useful to dissect complex biological phenomena into simple and easy to understand synthetic modules. Synthetic gene networks have been successfully implemented in prokaryotes and lower eukaryotes, with recent approaches moving ahead towards the mammalian environment. However, synthetic circuits in higher eukaryotes present a more challenging scenario, since its reliability is compromised because of the strong stochastic nature of transcription. Here, the authors review recent approaches that take advantage of the noisy response of synthetic regulatory circuits to learn key features of the complex machinery that orchestrates transcription in higher eukaryotes. Understanding the causes and consequences of biological noise will allow us to design more reliable mammalian synthetic circuits with revolutionary medical applications.
  • Keywords
    biological techniques; biomedical engineering; genetics; noise; stochastic processes; biological noise; complex biological phenomena; functional organism; higher eukaryote transcription; life forms; mammalian environment; mammalian synthetic circuits; mammalian system; modularity engineering principle; noisy response; prokaryote; revolutionary medical application; synthetic biology; synthetic gene network; synthetic module; synthetic regulatory circuits; technical application; transcription stochastic nature; transcriptional networks;
  • fLanguage
    English
  • Journal_Title
    Systems Biology, IET
  • Publisher
    iet
  • ISSN
    1751-8849
  • Type

    jour

  • DOI
    10.1049/iet-syb.2012.0026
  • Filename
    6518036