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

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

A generalized test of genotype-phenotype causality in population-sampled nuclear families.

PLoS genetics·2026
Same author

Beyond individual traits: differential associations of social support profiles with resilience in older learners.

Frontiers in psychology·2026
Same author

Unpacking GenAI-enabled deep learning engagement: role perceptions, human-GenAI synergy strategies, and underlying mechanisms.

Frontiers in psychology·2026
Same author

Organic Charge-Transfer Dielectric Cocrystals for Flexible Triboelectric Nanogenerators and Wearable Application.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Zeptomolar heavy metal ion detection with density of states sensing.

Nanoscale·2026
Same author

MSDDG: Multi-scale dual-discriminator GAN for point cloud completion of plant.

Plant phenomics (Washington, D.C.)·2026

Related Experiment Video

Updated: May 28, 2026

Using Confocal Analysis of Xenopus laevis to Investigate Modulators of Wnt and Shh Morphogen Gradients
08:10

Using Confocal Analysis of Xenopus laevis to Investigate Modulators of Wnt and Shh Morphogen Gradients

Published on: December 14, 2015

A computational statistics approach for estimating the spatial range of morphogen gradients.

Jitendra S Kanodia1, Yoosik Kim, Raju Tomer

  • 1Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Development (Cambridge, England)
|October 19, 2011
PubMed
Summary

Researchers developed a statistical framework to determine the effective range of morphogen gradients. This method provides a precise estimate for the spatial range of nuclear Dorsal in Drosophila embryos.

More Related Videos

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Optogenetic Signaling Activation in Zebrafish Embryos
07:18

Optogenetic Signaling Activation in Zebrafish Embryos

Published on: October 27, 2023

Related Experiment Videos

Last Updated: May 28, 2026

Using Confocal Analysis of Xenopus laevis to Investigate Modulators of Wnt and Shh Morphogen Gradients
08:10

Using Confocal Analysis of Xenopus laevis to Investigate Modulators of Wnt and Shh Morphogen Gradients

Published on: December 14, 2015

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Optogenetic Signaling Activation in Zebrafish Embryos
07:18

Optogenetic Signaling Activation in Zebrafish Embryos

Published on: October 27, 2023

Area of Science:

  • Developmental Biology
  • Genetics
  • Biophysics

Background:

  • Morphogen gradients are crucial for embryonic development, regulating cell signaling, gene expression, and differentiation.
  • Determining the spatial range of morphogen action is a key challenge in developmental biology.

Purpose of the Study:

  • To present a straightforward statistical framework for estimating the spatial range of morphogen gradients.
  • To apply this framework to quantify the range of nuclear Dorsal in Drosophila embryos.

Main Methods:

  • Development of a novel statistical framework for analyzing spatial gradient data.
  • Application of the framework to quantify the distribution of nuclear Dorsal, a key developmental transcription factor.

Main Results:

  • The study provides a point estimate and confidence interval for the spatial range of nuclear Dorsal.
  • The developed statistical framework is applicable to various developmental systems.

Conclusions:

  • The new statistical approach offers a robust method for assessing morphogen gradient ranges.
  • Understanding morphogen range is essential for deciphering developmental patterning mechanisms.