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 Video

Updated: Jul 22, 2026

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

Study Quiescence Heterogeneity by Coupling Single-Cell Measurements and Computer Modeling.

Jungeun Sarah Kwon1, Xia Wang1, Guang Yao2

  • 1Department of Molecular and Cellular Biology, University of Arizona, 1007 E. Lowell Street, P.O. Box 210106, Tucson, AZ, USA.

Methods in Molecular Biology (Clifton, N.J.)
|October 15, 2017
PubMed
Summary

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

Simulating High-Altitude Hypoxic Conditions Delays Wound Healing In Rats.

Journal of visualized experiments : JoVE·2026
Same author

Flexible bioelectronic sensors promote the advancement of wearable ultrasound technology in the medicine.

Microsystems & nanoengineering·2026
Same author

Recent advances in characterizing the immune microenvironment and biomarkers of endometrial carcinoma.

Frontiers in immunology·2026
Same author

Multi-Axis Stretchable Zippers for Personalized Wound Healing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Smart contact lenses unlocking non-invasive eye treatment.

Fundamental research·2026
Same author

Serum hypocretin, neurofilament heavy chain, and interleukin-1β as combined predictors of sleep disorders following acute ischemic stroke.

Frontiers in neuroscience·2026

This study explores how gene networks influence quiescence heterogeneity using single-cell measurements and computer simulations. The researchers developed a method to analyze gene activity in quiescent cells. They found that variations in gene network activity lead to diverse quiescent states. By combining empirical data with computational models, they predicted how gene regulation affects cell behavior. The results suggest that mathematical modeling can improve understanding of cellular functions. The study emphasizes the importance of integrating experimental and theoretical approaches. These findings may lead to new ways to study quiescence and other cellular processes.

Area of Science:

  • Systems biology within computational biology
  • Cellular signaling mechanisms in molecular biology
  • Single-cell analysis techniques in biotechnology

Background:

Understanding cellular quiescence remains a challenge in systems biology. Prior research has shown that quiescence involves complex gene regulatory networks. However, the heterogeneity of quiescent states across individual cells is not fully understood. Existing methods often fail to capture dynamic behaviors at the single-cell level. This gap motivated the development of new experimental and computational approaches. No prior work had resolved how gene networks influence quiescence heterogeneity. Mathematical modeling offers a way to simulate these interactions. Yet, integrating single-cell measurements with simulations remains limited. This paper introduces a novel framework to address these limitations.

Purpose Of The Study:

The aim of this study is to develop a method for analyzing quiescence heterogeneity using single-cell data and computational models. The specific problem is the lack of tools to explore gene network dynamics in quiescent cells. The motivation comes from the need to understand how gene regulation leads to diverse quiescent states. Traditional bulk measurements obscure individual cell behaviors. Single-cell approaches can reveal this variability. However, interpreting these data requires advanced modeling techniques. The study proposes combining measurements with simulations to better understand quiescence. This approach allows for testing hypotheses about gene network behaviors.

Keywords:
Cell cycleClick-iT EdU assayDNA contentDeterministic simulationFluorescent protein reporterHeterogeneityOrdinary differential equation (ODE)QuiescenceStochastic simulationQuiescence heterogeneitySingle-cell analysisGene network modelingComputational biology

Frequently Asked Questions

The paper studies how gene network dynamics influence quiescence heterogeneity at the single-cell level.

Single-cell measurements and mathematical modeling are used to explore gene network behaviors.

Single-cell analysis is necessary to capture heterogeneity that bulk measurements obscure.

Simulations test how gene network changes affect quiescence by predicting cell behavior.

Single-cell measurements capture gene expression data from individual quiescent cells.

More Related Videos

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics
10:24

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria
05:44

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Published on: December 27, 2024

Related Experiment Videos

Last Updated: Jul 22, 2026

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

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics
10:24

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria
05:44

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

Published on: December 27, 2024

Main Methods:

The study uses single-cell measurements to capture gene expression data from individual cells. These data are then analyzed using mathematical modeling to infer network behaviors. Computer simulations are employed to test different regulatory scenarios. The experimental setup involves isolating and measuring cells in quiescent states. Computational tools are used to model interactions between genes and proteins. The simulations help predict how changes in gene activity affect quiescence. The approach integrates empirical data with theoretical predictions. This combination allows for a more detailed analysis of quiescence heterogeneity.

Main Results:

The strongest finding is the successful integration of single-cell data with computational models. The simulations revealed distinct patterns of gene activity in quiescent cells. These patterns suggest different regulatory mechanisms influencing quiescence. The models accurately predicted how gene expression changes affect cell behavior. The study found that heterogeneity arises from variations in gene network activity. Single-cell measurements provided detailed insights into these variations. The simulations confirmed that gene interactions drive quiescence diversity. These results suggest that computational approaches can enhance understanding of quiescence.

Conclusions:

The authors propose that combining single-cell measurements with modeling improves understanding of quiescence. They suggest that gene network dynamics explain heterogeneity in quiescent states. The study shows that simulations can predict how gene regulation affects quiescence. The results indicate that computational models are valuable for analyzing cellular behaviors. The authors emphasize the importance of integrating empirical and theoretical approaches. They propose that this framework can be applied to other cellular processes. The study concludes that mathematical modeling enhances insights into gene networks. These findings suggest new ways to explore cellular functions.

The authors suggest that integrating measurements with modeling enhances understanding of quiescence heterogeneity.