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 Concept Videos

Molecular Factors Affecting Cell Division01:27

Molecular Factors Affecting Cell Division

3.9K
Several external and internal factors influence the initiation and inhibition of cell division. For instance, the death of nearby cells or the release of human growth hormone (hGH) promotes cell division. In contrast, lack of hGH or crowding of cells can inhibit cell division.
Several proteins function as internal regulators to ensure each cell cycle stage is completed faithfully before proceeding to the next. Regulator molecules may act directly or influence the activity or production of other...
3.9K
Real Time RT-PCR02:57

Real Time RT-PCR

51.6K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
51.6K

You might also read

Related Articles

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

Sort by
Same author

Single-cell heterogeneity in ribosome levels and protein synthesis during nutrient starvation is driven by cAMP signaling.

Science advances·2026
Same author

A dominant role of cell death in limiting Chandipura virus propagation at cell-saturating high multiplicity of infection.

mBio·2026
Same author

The role of cell growth rate on accumulation of the mitotic cyclin Cdc13 in fission yeast.

bioRxiv : the preprint server for biology·2026
Same author

Impact of variability in cell generation times on cell-to-cell variability of protein concentrations.

bioRxiv : the preprint server for biology·2026
Same author

Enhancer placement impacts transcriptional dynamics in Drosophila embryos.

Nature communications·2026
Same author

Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.

bioRxiv : the preprint server for biology·2026
Same journal

Phenotypic plasticity trade-offs in an age-structured model of bacterial growth under stress.

Journal of mathematical biology·2026
Same journal

Intraspecific interactions facilitate mutualism across multilayer networks under weak selection.

Journal of mathematical biology·2026
Same journal

A two-species competition model on a compact metric graph for the invasion and competition of Aedes Aegypti and Aedes Albopictus mosquitoes in Florida.

Journal of mathematical biology·2026
Same journal

Superinfection and the hypnozoite reservoir for Plasmodium vivax: a multitype branching process approximation.

Journal of mathematical biology·2026
Same journal

Correction to: Superinfection and the hypnozoite reservoir for Plasmodium vivax: a general framework.

Journal of mathematical biology·2026
Same journal

Stoichiometric balance and sustained rhythms.

Journal of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: Apr 24, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.0K

Quantifying gene expression variability arising from randomness in cell division times.

Duarte Antunes1, Abhyudai Singh

  • 1Control Systems Technology, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands, D.Antunes@tue.nl.

Journal of Mathematical Biology
|September 4, 2014
PubMed
Summary
This summary is machine-generated.

Cell division time variability significantly impacts gene expression noise. This study shows random cell division can be a major driver of protein level differences in cell populations.

More Related Videos

Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting
08:14

Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting

Published on: September 25, 2012

16.5K
Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.3K

Related Experiment Videos

Last Updated: Apr 24, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

16.0K
Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting
08:14

Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting

Published on: September 25, 2012

16.5K
Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

3.3K

Area of Science:

  • Cell Biology
  • Biophysics
  • Systems Biology

Background:

  • Cell-to-cell variability in mRNA and protein levels is a known phenomenon in homogeneous cell populations.
  • Stochasticity in gene expression is a primary focus for understanding this variability.
  • Recent findings highlight significant heterogeneity in cell division times.

Purpose of the Study:

  • To investigate the impact of random cell division times on population-level gene expression variability.
  • To develop a mathematical model for quantifying the effects of division time heterogeneity.
  • To determine if cell division randomness is a significant contributor to protein level differences.

Main Methods:

  • Developed a mathematical model where mRNA/protein levels follow linear differential equations.
  • Incorporated cell divisions occurring at random intervals, halving molecular counts.
  • Established a method to compute statistical moments (mean, variance, skewness) of molecular levels.
  • Showed that the evolution of statistical moments follows an upper triangular Volterra system.

Main Results:

  • Demonstrated that randomness in cell division times can quantitatively explain population-level variability.
  • Calculated statistical moments for physiologically relevant parameters.
  • Confirmed that division time heterogeneity is a significant factor in protein level differences.

Conclusions:

  • Randomness in cell division is a crucial, often overlooked, source of gene expression noise.
  • The developed mathematical framework allows for precise quantification of this variability.
  • Understanding division time effects is essential for comprehending cellular heterogeneity.