Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A chance-selection model for cell differentiation.

J J Kupiec1

  • 1ICGM-CNRS UPR 415 Institut Cochin de Génétique Moléculaire, 22 rue méchain, Paris 75014, France.

Cell Death and Differentiation
|October 1, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Local and spatial factors determining HIV-1 protease substrate recognition.

The Biochemical journal·2001
Same author

Structural and functional analysis of surface domains unique to bacteriophage T4 thymidylate synthase.

Biochemistry·1999
Same author

A Darwinian theory for the origin of cellular differentiation.

Molecular & general genetics : MGG·1997
Same author

Reverse transcriptase jumps and gaps.

The Journal of general virology·1996
Same author

Conversion of thymidylate synthase into an HIV protease substrate.

The Journal of biological chemistry·1996
Same author

Sequence analysis of the simian foamy virus type 1 genome.

Gene·1991
Same journal

A planar dimer of bovine ATP synthase.

Cell death and differentiation·2026
Same journal

GCN5 and TADA2B constitutively regulate XRCC1 function during DNA repair to maintain cell survival.

Cell death and differentiation·2026
Same journal

MEGF8 controls osteogenic differentiation through post-transcriptional regulation of BMP-SMAD signaling in craniosynostosis.

Cell death and differentiation·2026
Same journal

Macrophage-secreted brain-derived neurotrophic factor promotes tumor growth in triple-negative breast cancer by inducing axonogenesis.

Cell death and differentiation·2026
Same journal

Species-specific regulation of necroptosis by STK38-dependent RIPK1 phosphorylation.

Cell death and differentiation·2026
Same journal

Ssu72 phosphatase orchestrates obesogenic adipogenesis and metabolic homeostasis during nutrient excess.

Cell death and differentiation·2026
See all related articles

This study proposes a stochastic model for cell differentiation, where random molecular events create cell diversity and cell interactions provide developmental order. It explains how regulator diffusion and phosphorylation stabilize gene expression patterns.

Area of Science:

  • Developmental Biology
  • Molecular Biology
  • Systems Biology

Background:

  • Cell differentiation involves generating diverse cell types within an ordered developing embryo.
  • Stochasticity at the molecular level is a known source of biological diversity.
  • Cell-cell interactions are crucial for pattern formation during embryogenesis.

Purpose of the Study:

  • To propose a chance-selection model explaining cell differentiation.
  • To elucidate the roles of molecular stochasticity and cell interactions in this process.
  • To discuss the comparative explanatory power of deterministic versus stochastic models.

Main Methods:

  • A theoretical model based on stochastic molecular interactions between transcriptional regulators and DNA.
  • Incorporation of random diffusion of regulators along DNA.

Related Experiment Videos

  • Modeling the effect of phosphorylation/dephosphorylation on regulator diffusion and gene expression stabilization.

    • Main Results:

    • The model explains how random diffusion of transcriptional regulators drives differential gene expression.
    • Phosphorylation/dephosphorylation events, triggered by cell interactions, stabilize gene expression in differentiated cells.
    • The model integrates known molecular mechanisms like transcription factor diffusion and post-translational modification.

    Conclusions:

    • Stochastic molecular events coupled with cell interactions provide a robust framework for understanding cell differentiation.
    • The proposed model offers a mechanistic explanation for the emergence of order from molecular randomness.
    • This stochastic approach complements traditional deterministic models in developmental biology.