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Related Experiment Videos

Switches in bacteriophage lambda development.

Amos B Oppenheim1, Oren Kobiler, Joel Stavans

  • 1Department of Molecular Genetics and Biotechnology, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel. oppenhea@mail.nih.gov

Annual Review of Genetics
|November 16, 2005
PubMed
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Bacteriophage lambda's lysis-lysogeny decision is a model genetic network. Cro protein indirectly controls the switch to lytic development by reducing CII levels, impacting CI transcription.

Area of Science:

  • Molecular Biology
  • Genetics
  • Systems Biology

Background:

  • Bacteriophage lambda's lysis-lysogeny decision is a key model for genetic regulatory networks.
  • This decision involves environmental signals and phage multiplicity of infection.
  • The lysogenic state is stable, but the prophage can enter lytic development upon DNA damage.

Purpose of the Study:

  • To elucidate the regulatory mechanisms governing bacteriophage lambda's lysis-lysogeny decision.
  • To understand the roles of CI, Cro, and CII proteins in this bistable genetic switch.
  • To explore the complexity of decision-making in simple viral genetic networks.

Main Methods:

  • Analysis of bacteriophage lambda genetic regulatory network.
  • Investigation of the roles of key regulatory proteins (CI, Cro, CII).

Related Experiment Videos

  • Discussion of theoretical modeling challenges for viral decision-making processes.

    • Main Results:

    • The CI and Cro proteins act as a bistable genetic switch, defining lysogenic and lytic states, respectively.
    • Cro promotes lytic development indirectly by downregulating CII levels, which are essential for activating cI transcription.
    • This indirect mechanism highlights the complexity of the genetic network.

    Conclusions:

    • Bacteriophage lambda utilizes a sophisticated genetic network for decision-making.
    • The regulatory interplay between CI, Cro, and CII is crucial for maintaining lysogeny or initiating lysis.
    • The study presents challenges and opportunities for theoretical modeling of genetic networks.